LEXICAL DISPERSION · RANGE · WORD DISTRIBUTION · FREQUENCY · DOCUMENT FREQUENCY · CORPUS LINGUISTICS · GENERAL VOCABULARY · SPECIALISED VOCABULARY
Lexical range describes how widely a word appears across texts or corpus sections, while lexical dispersion describes how evenly its occurrences are spread. These measures answer a question raw frequency cannot: where does the word live?
Two words can each occur 1,000 times in a corpus. One might appear a few times in nearly every document; the other might appear 900 times inside one specialised report and hardly anywhere else. Raw frequency treats them as equally common. Range and dispersion reveal that their general usefulness is very different.
This guide explains frequency, normalised frequency, range, dispersion, document frequency, burstiness, domain specificity, register concentration, regional distribution and vocabulary-list design. Existing eduKateSG Word Frequency and Corpus Linguistics articles remain untouched.
Frequency counts how often. Dispersion asks how widely.
2. Frequency is not distribution
Two words can have the same total frequency while being distributed very differently. One may occur in almost every text; the other may be extremely common in only a few documents.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
3. Range
Range usually refers to the number or proportion of texts or corpus sections in which a lexical item occurs. High range suggests broad distribution across documents.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
4. Dispersion
Dispersion describes how evenly occurrences are spread across the corpus. A word may have high range but still be heavily concentrated in one section.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
5. Why raw counts mislead
A single long document can inflate frequency. Without distribution information, a term may look generally common even when it is topic-bound.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
6. General-service vocabulary
Broadly useful general vocabulary tends to combine relatively high frequency with wide range and stronger dispersion across topics.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
7. Technical vocabulary
Technical words can have high frequency inside a domain but low range across a general corpus. Distribution reveals specialization.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
8. Register vocabulary
Words associated with legal, academic, conversational or journalistic registers can show uneven dispersion even when their total counts are substantial.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
9. Regional vocabulary
A term may be common in one regional corpus and rare elsewhere. Distribution across varieties matters when describing global English.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
10. Document frequency
Counting how many documents contain a word is a simple way to capture range. It prevents one highly repetitive document from dominating interpretation.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
11. Normalised frequency
Frequency is often normalised per million words or another base so corpora of different sizes can be compared. Normalisation still does not solve uneven distribution by itself.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
12. Dispersion measures
Corpus linguistics uses several dispersion statistics, including Juilland’s D, coefficient-based measures and deviation-of-proportions approaches. Each makes different assumptions.
The practical implication is that a useful general word should not merely be frequent; it should recur across enough independent texts and contexts that learners are likely to meet it repeatedly.
13. DP-style dispersion
Deviation of proportions compares observed distribution with what would be expected if occurrences followed corpus size. Lower deviation indicates more even spread in common formulations.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
14. Range and word lists
Modern vocabulary lists increasingly use range and dispersion alongside frequency so selected words are not merely frequent in a narrow cluster of texts.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
15. Frequency bands
A frequency band can contain both general and specialised words. Distribution helps identify which high-count items travel broadly.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
16. Burstiness
Some words occur in bursts: once a topic begins, the same item repeats many times. Burstiness can create high local frequency without high general utility.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
17. Topic effects
Topic is one of the largest drivers of lexical concentration. Words such as inflation, photosynthesis or offside can become temporarily dominant inside relevant texts.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
18. Genre effects
Genre changes distribution. A word may be broadly dispersed in research articles but rare in conversation, or vice versa.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
19. Time effects
Distribution can shift historically. New technologies, crises and cultural events can move terms from narrow clusters into broad public use.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
20. Pedagogical value
For general learners, wide-range words often deserve higher priority than equally frequent words concentrated in narrow domains.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
21. Domain learners
For specialists, local domain dispersion may matter more than general-corpus dispersion. A medical learner needs words that recur across many medical texts even if rare globally.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
22. AI-era corpora
Large digital corpora make distribution analysis easier, but corpus design still matters. A biased corpus can make a local cluster look globally representative.
Distribution should always be interpreted relative to corpus design. A word can look broadly dispersed inside a specialist corpus while remaining highly specialised in general language.
23. Distribution casebook — Cases 1–20
1. the
Type: function word. Frequency: very high. Range: very wide. Dispersion: very even. Interpretation: core grammar.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue the.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
2. people
Type: general noun. Frequency: high. Range: wide. Dispersion: fairly even. Interpretation: general vocabulary.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue people.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
3. time
Type: general noun. Frequency: very high. Range: wide. Dispersion: fairly even. Interpretation: general vocabulary.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue time.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
4. change
Type: general noun/verb. Frequency: high. Range: wide. Dispersion: moderately even. Interpretation: cross-domain general word.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue change.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
5. important
Type: general adjective. Frequency: high. Range: wide. Dispersion: moderately even. Interpretation: general academic/everyday.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue important.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
6. evidence
Type: academic/general noun. Frequency: medium/high. Range: wide across formal texts. Dispersion: moderate. Interpretation: general academic.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue evidence.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
7. factor
Type: academic/general noun. Frequency: medium. Range: wide across school subjects. Dispersion: moderate. Interpretation: academic utility.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue factor.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
8. model
Type: polysemous noun. Frequency: high. Range: wide. Dispersion: uneven by sense. Interpretation: general + technical.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue model.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
9. function
Type: academic/technical noun. Frequency: medium. Range: wide but domain-shaped. Dispersion: uneven. Interpretation: cross-domain technical.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue function.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
10. system
Type: general/technical noun. Frequency: high. Range: wide. Dispersion: moderate. Interpretation: broad abstract vocabulary.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue system.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
11. algorithm
Type: technical noun. Frequency: medium. Range: narrower. Dispersion: technology-heavy. Interpretation: specialised but public.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue algorithm.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
12. photosynthesis
Type: science term. Frequency: low globally. Range: narrow. Dispersion: highly concentrated. Interpretation: biology.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue photosynthesis.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
13. mitochondria
Type: science term. Frequency: low globally. Range: narrow. Dispersion: highly concentrated. Interpretation: biology.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue mitochondria.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
14. jurisdiction
Type: legal/civic term. Frequency: medium. Range: narrow/moderate. Dispersion: legal concentration. Interpretation: law.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue jurisdiction.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
15. injunction
Type: legal term. Frequency: low. Range: narrow. Dispersion: highly concentrated. Interpretation: law.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue injunction.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
16. liquidity
Type: finance term. Frequency: medium. Range: narrow/moderate. Dispersion: finance concentration. Interpretation: finance.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue liquidity.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
17. basis point
Type: finance multiword term. Frequency: medium in finance. Range: narrow. Dispersion: highly concentrated. Interpretation: finance.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue basis point.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
18. latency
Type: technical noun. Frequency: medium. Range: narrow/moderate. Dispersion: computing/network-heavy. Interpretation: technology.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue latency.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
19. bandwidth
Type: technical/generalised noun. Frequency: medium/high. Range: moderate. Dispersion: technology-heavy. Interpretation: technical + metaphorical.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue bandwidth.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
20. cache
Type: computing noun/verb. Frequency: medium. Range: narrow/moderate. Dispersion: technical. Interpretation: computing.
Frequency-only error: imagine selecting vocabulary by total count alone. Explain how that could overvalue or undervalue cache.
Document test: count how many independent texts contain the word. High document frequency suggests broader usefulness than repeated occurrence inside one source.
Corpus-design test: change the corpus composition—news, conversation, school texts or specialist documents—and predict how the item’s apparent importance changes.
Teaching decision: decide whether the word deserves general productive mastery, specialist mastery or mainly receptive recognition.
24. Distribution casebook — Cases 21–40
21. offside
Type: sports term. Frequency: medium. Range: narrow. Dispersion: sports-heavy. Interpretation: sport.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
22. penalty
Type: sports/legal/general noun. Frequency: high. Range: wide. Dispersion: sense-dependent. Interpretation: poly-domain.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
23. goal
Type: general/sports noun. Frequency: high. Range: wide. Dispersion: moderate. Interpretation: general + sports.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
24. tempo
Type: music term. Frequency: medium. Range: narrow/moderate. Dispersion: music-heavy. Interpretation: music.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
25. timbre
Type: music term. Frequency: low. Range: narrow. Dispersion: highly concentrated. Interpretation: music.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
26. quarantine
Type: public-health noun. Frequency: event-sensitive. Range: wide during crises. Dispersion: time-variable. Interpretation: public health.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
27. pandemic
Type: public-health noun. Frequency: historically variable. Range: broad in crisis periods. Dispersion: time-clustered. Interpretation: event-driven.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
28. lockdown
Type: public-policy noun. Frequency: historically bursty. Range: broad in 2020-era texts. Dispersion: time-clustered. Interpretation: event-driven.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
29. emoji
Type: digital noun. Frequency: medium/high. Range: wide. Dispersion: moderate. Interpretation: mainstream digital.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
30. hashtag
Type: digital noun. Frequency: medium. Range: moderate. Dispersion: platform-heavy. Interpretation: digital.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
31. doomscrolling
Type: internet coinage. Frequency: low/moderate. Range: narrow. Dispersion: social-media concentrated. Interpretation: internet culture.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
32. selfie
Type: mainstream digital noun. Frequency: high. Range: wide. Dispersion: moderate. Interpretation: general modern vocabulary.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
33. podcast
Type: media noun. Frequency: high. Range: wide. Dispersion: moderate. Interpretation: media.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
34. webinar
Type: professional/digital noun. Frequency: medium. Range: moderate. Dispersion: professional-online contexts. Interpretation: work/education.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
35. rubric
Type: education noun. Frequency: medium. Range: narrow/moderate. Dispersion: education-heavy. Interpretation: education.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
36. scaffolding
Type: education/construction noun. Frequency: medium. Range: moderate. Dispersion: sense-clustered. Interpretation: poly-domain.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
37. formative assessment
Type: education term. Frequency: medium in education. Range: narrow. Dispersion: highly concentrated. Interpretation: education.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
38. hypothesis
Type: academic noun. Frequency: medium. Range: wide across science/education. Dispersion: moderate. Interpretation: academic.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
39. methodology
Type: academic noun. Frequency: medium. Range: wide in research texts. Dispersion: register-concentrated. Interpretation: research.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
40. whereas
Type: formal conjunction. Frequency: medium. Range: wide in formal writing. Dispersion: register-shaped. Interpretation: academic/formal.
Burstiness check: ask whether the word tends to repeat many times once its topic begins. Topic burstiness can inflate counts without making the item broadly useful.
Register check: compare spoken, academic, legal and journalistic corpora. Distribution across registers may explain why a word feels common to one learner and rare to another.
Sense check: for polysemous words, different senses may have different distributions. A broad form can hide specialised sense clusters.
Priority: combine frequency with range and learner goals before deciding study depth.
25. Distribution casebook — Cases 41–60
41. gonna
Type: informal form. Frequency: high in conversation. Range: narrow by mode. Dispersion: speech-heavy. Interpretation: informal spoken.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
42. kinda
Type: informal form. Frequency: medium/high in informal data. Range: narrow by register. Dispersion: chat/speech-heavy. Interpretation: informal.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
43. notwithstanding
Type: formal item. Frequency: low. Range: narrow. Dispersion: legal/formal-heavy. Interpretation: formal.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
44. hereby
Type: formal adverb. Frequency: low. Range: narrow. Dispersion: legal/official concentration. Interpretation: legal.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
45. cheers
Type: pragmatic expression. Frequency: regional. Range: wide in some varieties. Dispersion: region-shaped. Interpretation: regional informal.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
46. lorry
Type: regional noun. Frequency: high UK/Singapore. Range: regionally wide. Dispersion: geographically uneven. Interpretation: regional.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
47. truck
Type: regional/general noun. Frequency: high US/global. Range: wide. Dispersion: regional variation. Interpretation: regional/general.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
48. HDB
Type: local abbreviation. Frequency: high Singapore. Range: narrow globally. Dispersion: geographically concentrated. Interpretation: Singapore institutional.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
49. MRT
Type: local/regional abbreviation. Frequency: high Singapore. Range: narrow globally. Dispersion: geographically concentrated. Interpretation: Singapore transport.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
50. NHS
Type: institutional abbreviation. Frequency: high UK. Range: narrow globally. Dispersion: regional/institutional. Interpretation: UK.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
51. SAT
Type: education abbreviation. Frequency: high US education. Range: narrow globally. Dispersion: institutional. Interpretation: US education.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
52. AI
Type: technology abbreviation. Frequency: very high recently. Range: wide. Dispersion: time-growing. Interpretation: mainstream technical.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
53. blockchain
Type: technology noun. Frequency: medium. Range: moderate. Dispersion: topic-clustered. Interpretation: technology.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
54. quantum
Type: science adjective/noun. Frequency: medium. Range: moderate. Dispersion: science-heavy. Interpretation: science + public.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
55. inflation
Type: economics noun. Frequency: high in news cycles. Range: wide/moderate. Dispersion: time/topic sensitive. Interpretation: economics/public.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
56. interest rate
Type: finance/economics phrase. Frequency: high. Range: wide in business/news. Dispersion: moderate. Interpretation: economics.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
57. biodiversity
Type: environment noun. Frequency: medium. Range: moderate. Dispersion: environment-heavy. Interpretation: environment.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
58. sustainability
Type: general/academic noun. Frequency: high. Range: wide. Dispersion: register/domain shaped. Interpretation: cross-domain.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
59. carbon-neutral
Type: environment adjective. Frequency: medium. Range: moderate. Dispersion: policy/business concentration. Interpretation: environment/policy.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
60. photosphere
Type: astronomy term. Frequency: low. Range: very narrow. Dispersion: highly concentrated. Interpretation: astronomy.
Regional check: compare at least two national or community corpora. A locally core word can be globally narrow without being marginal in the learner’s real environment.
Time check: compare different years. Event-driven vocabulary may move from narrow to broad distribution and later contract again.
Specialisation check: if the word is frequent in one domain but rare elsewhere, treat it as domain-core rather than general-core vocabulary.
Transfer: find another word with a similar distribution pattern and explain what learner population would benefit most from it.
26. Using range and dispersion to build vocabulary lists
1. the
Selection question: would the enter a general-service list, a domain list or neither? Use the combination very high frequency, very wide range and very even dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
2. people
Selection question: would people enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and fairly even dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
3. time
Selection question: would time enter a general-service list, a domain list or neither? Use the combination very high frequency, wide range and fairly even dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
4. change
Selection question: would change enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderately even dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
5. important
Selection question: would important enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderately even dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
6. evidence
Selection question: would evidence enter a general-service list, a domain list or neither? Use the combination medium/high frequency, wide across formal texts range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
7. factor
Selection question: would factor enter a general-service list, a domain list or neither? Use the combination medium frequency, wide across school subjects range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
8. model
Selection question: would model enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and uneven by sense dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
9. function
Selection question: would function enter a general-service list, a domain list or neither? Use the combination medium frequency, wide but domain-shaped range and uneven dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
10. system
Selection question: would system enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
11. algorithm
Selection question: would algorithm enter a general-service list, a domain list or neither? Use the combination medium frequency, narrower range and technology-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
12. photosynthesis
Selection question: would photosynthesis enter a general-service list, a domain list or neither? Use the combination low globally frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
13. mitochondria
Selection question: would mitochondria enter a general-service list, a domain list or neither? Use the combination low globally frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
14. jurisdiction
Selection question: would jurisdiction enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and legal concentration dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
15. injunction
Selection question: would injunction enter a general-service list, a domain list or neither? Use the combination low frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
16. liquidity
Selection question: would liquidity enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and finance concentration dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
17. basis point
Selection question: would basis point enter a general-service list, a domain list or neither? Use the combination medium in finance frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
18. latency
Selection question: would latency enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and computing/network-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
19. bandwidth
Selection question: would bandwidth enter a general-service list, a domain list or neither? Use the combination medium/high frequency, moderate range and technology-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
20. cache
Selection question: would cache enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and technical dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
21. offside
Selection question: would offside enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow range and sports-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
22. penalty
Selection question: would penalty enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and sense-dependent dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
23. goal
Selection question: would goal enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
24. tempo
Selection question: would tempo enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and music-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
25. timbre
Selection question: would timbre enter a general-service list, a domain list or neither? Use the combination low frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
26. quarantine
Selection question: would quarantine enter a general-service list, a domain list or neither? Use the combination event-sensitive frequency, wide during crises range and time-variable dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
27. pandemic
Selection question: would pandemic enter a general-service list, a domain list or neither? Use the combination historically variable frequency, broad in crisis periods range and time-clustered dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
28. lockdown
Selection question: would lockdown enter a general-service list, a domain list or neither? Use the combination historically bursty frequency, broad in 2020-era texts range and time-clustered dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
29. emoji
Selection question: would emoji enter a general-service list, a domain list or neither? Use the combination medium/high frequency, wide range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
30. hashtag
Selection question: would hashtag enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and platform-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
31. doomscrolling
Selection question: would doomscrolling enter a general-service list, a domain list or neither? Use the combination low/moderate frequency, narrow range and social-media concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
32. selfie
Selection question: would selfie enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
33. podcast
Selection question: would podcast enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
34. webinar
Selection question: would webinar enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and professional-online contexts dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
35. rubric
Selection question: would rubric enter a general-service list, a domain list or neither? Use the combination medium frequency, narrow/moderate range and education-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
36. scaffolding
Selection question: would scaffolding enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and sense-clustered dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
37. formative assessment
Selection question: would formative assessment enter a general-service list, a domain list or neither? Use the combination medium in education frequency, narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
38. hypothesis
Selection question: would hypothesis enter a general-service list, a domain list or neither? Use the combination medium frequency, wide across science/education range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
39. methodology
Selection question: would methodology enter a general-service list, a domain list or neither? Use the combination medium frequency, wide in research texts range and register-concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
40. whereas
Selection question: would whereas enter a general-service list, a domain list or neither? Use the combination medium frequency, wide in formal writing range and register-shaped dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
41. gonna
Selection question: would gonna enter a general-service list, a domain list or neither? Use the combination high in conversation frequency, narrow by mode range and speech-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
42. kinda
Selection question: would kinda enter a general-service list, a domain list or neither? Use the combination medium/high in informal data frequency, narrow by register range and chat/speech-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
43. notwithstanding
Selection question: would notwithstanding enter a general-service list, a domain list or neither? Use the combination low frequency, narrow range and legal/formal-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
44. hereby
Selection question: would hereby enter a general-service list, a domain list or neither? Use the combination low frequency, narrow range and legal/official concentration dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
45. cheers
Selection question: would cheers enter a general-service list, a domain list or neither? Use the combination regional frequency, wide in some varieties range and region-shaped dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
46. lorry
Selection question: would lorry enter a general-service list, a domain list or neither? Use the combination high UK/Singapore frequency, regionally wide range and geographically uneven dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
47. truck
Selection question: would truck enter a general-service list, a domain list or neither? Use the combination high US/global frequency, wide range and regional variation dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
48. HDB
Selection question: would HDB enter a general-service list, a domain list or neither? Use the combination high Singapore frequency, narrow globally range and geographically concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
49. MRT
Selection question: would MRT enter a general-service list, a domain list or neither? Use the combination high Singapore frequency, narrow globally range and geographically concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
50. NHS
Selection question: would NHS enter a general-service list, a domain list or neither? Use the combination high UK frequency, narrow globally range and regional/institutional dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
51. SAT
Selection question: would SAT enter a general-service list, a domain list or neither? Use the combination high US education frequency, narrow globally range and institutional dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
52. AI
Selection question: would AI enter a general-service list, a domain list or neither? Use the combination very high recently frequency, wide range and time-growing dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
53. blockchain
Selection question: would blockchain enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and topic-clustered dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
54. quantum
Selection question: would quantum enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and science-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
55. inflation
Selection question: would inflation enter a general-service list, a domain list or neither? Use the combination high in news cycles frequency, wide/moderate range and time/topic sensitive dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
56. interest rate
Selection question: would interest rate enter a general-service list, a domain list or neither? Use the combination high frequency, wide in business/news range and moderate dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
57. biodiversity
Selection question: would biodiversity enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and environment-heavy dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
58. sustainability
Selection question: would sustainability enter a general-service list, a domain list or neither? Use the combination high frequency, wide range and register/domain shaped dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
59. carbon-neutral
Selection question: would carbon-neutral enter a general-service list, a domain list or neither? Use the combination medium frequency, moderate range and policy/business concentration dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
60. photosphere
Selection question: would photosphere enter a general-service list, a domain list or neither? Use the combination low frequency, very narrow range and highly concentrated dispersion rather than one metric alone.
Robustness test: remove the text or subcorpus where the word is most frequent. If its rank collapses, the item may be overly dependent on one cluster.
Learner-fit test: compare the corpus population with the learner’s future reading and speaking environment. Distribution is useful only when the corpus resembles the language the learner actually needs.
Depth decision: broad, well-dispersed items justify deeper productive learning; narrow specialist items may need deep learning only for learners entering that domain.
27. Frequently asked questions
What is lexical range?
How many texts or corpus sections contain a word.
What is lexical dispersion?
How evenly a word’s occurrences are spread across a corpus.
Is dispersion the same as frequency?
No. Frequency counts occurrences; dispersion measures their distribution.
Why can frequency be misleading?
A word may be repeated many times in only a few documents.
What is document frequency?
The number or proportion of documents in which a word occurs.
What is burstiness?
Concentrated repetition of a word within limited texts or topical stretches.
Why use range in word lists?
It favours vocabulary that recurs across many texts instead of narrow local clusters.
Can technical words have high frequency?
Yes, inside specialist corpora; distribution reveals the specialization.
Does regional vocabulary have low value?
Not for local learners. A regionally concentrated word can be highly useful in that community.
What is the simplest rule?
Count not only how often a word appears, but how widely it travels.
28. Research grounding
Cambridge corpus-linguistics work explicitly distinguishes frequency, range and dispersion. Brezina’s Statistics in Corpus Linguistics discusses absolute and relative frequency together with multiple dispersion measures. Recent Language Teaching research defines range as the number of texts containing a lexical item and dispersion as its distribution through the corpus, noting that both are increasingly used in word-list design so selected items occur across texts rather than being concentrated in only a few.
- Cambridge — Frequency, dispersion and diversity
- Cambridge — Range and dispersion in vocabulary-list design
- Cambridge — Foundational quantitative corpus concepts
29. eduKateSG routes
30. Final model
Frequency tells us how much language a word occupies. Range and dispersion tell us how many different linguistic places it occupies.
For vocabulary planning, that difference is crucial: broad distribution usually signals reusable general value, while concentration reveals topic, register, regional or domain specificity.
A word can be frequent without being widespread.
31. Final distribution audit
1. the
Counterfactual corpus: imagine doubling the specialist texts most favourable to the. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from very wide range and very even dispersion.
2. people
Counterfactual corpus: imagine doubling the specialist texts most favourable to people. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and fairly even dispersion.
3. time
Counterfactual corpus: imagine doubling the specialist texts most favourable to time. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and fairly even dispersion.
4. change
Counterfactual corpus: imagine doubling the specialist texts most favourable to change. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderately even dispersion.
5. important
Counterfactual corpus: imagine doubling the specialist texts most favourable to important. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderately even dispersion.
6. evidence
Counterfactual corpus: imagine doubling the specialist texts most favourable to evidence. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across formal texts range and moderate dispersion.
7. factor
Counterfactual corpus: imagine doubling the specialist texts most favourable to factor. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across school subjects range and moderate dispersion.
8. model
Counterfactual corpus: imagine doubling the specialist texts most favourable to model. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and uneven by sense dispersion.
9. function
Counterfactual corpus: imagine doubling the specialist texts most favourable to function. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide but domain-shaped range and uneven dispersion.
10. system
Counterfactual corpus: imagine doubling the specialist texts most favourable to system. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
11. algorithm
Counterfactual corpus: imagine doubling the specialist texts most favourable to algorithm. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrower range and technology-heavy dispersion.
12. photosynthesis
Counterfactual corpus: imagine doubling the specialist texts most favourable to photosynthesis. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
13. mitochondria
Counterfactual corpus: imagine doubling the specialist texts most favourable to mitochondria. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
14. jurisdiction
Counterfactual corpus: imagine doubling the specialist texts most favourable to jurisdiction. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and legal concentration dispersion.
15. injunction
Counterfactual corpus: imagine doubling the specialist texts most favourable to injunction. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
16. liquidity
Counterfactual corpus: imagine doubling the specialist texts most favourable to liquidity. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and finance concentration dispersion.
17. basis point
Counterfactual corpus: imagine doubling the specialist texts most favourable to basis point. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
18. latency
Counterfactual corpus: imagine doubling the specialist texts most favourable to latency. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and computing/network-heavy dispersion.
19. bandwidth
Counterfactual corpus: imagine doubling the specialist texts most favourable to bandwidth. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and technology-heavy dispersion.
20. cache
Counterfactual corpus: imagine doubling the specialist texts most favourable to cache. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and technical dispersion.
21. offside
Counterfactual corpus: imagine doubling the specialist texts most favourable to offside. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and sports-heavy dispersion.
22. penalty
Counterfactual corpus: imagine doubling the specialist texts most favourable to penalty. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and sense-dependent dispersion.
23. goal
Counterfactual corpus: imagine doubling the specialist texts most favourable to goal. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
24. tempo
Counterfactual corpus: imagine doubling the specialist texts most favourable to tempo. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and music-heavy dispersion.
25. timbre
Counterfactual corpus: imagine doubling the specialist texts most favourable to timbre. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
26. quarantine
Counterfactual corpus: imagine doubling the specialist texts most favourable to quarantine. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide during crises range and time-variable dispersion.
27. pandemic
Counterfactual corpus: imagine doubling the specialist texts most favourable to pandemic. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from broad in crisis periods range and time-clustered dispersion.
28. lockdown
Counterfactual corpus: imagine doubling the specialist texts most favourable to lockdown. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from broad in 2020-era texts range and time-clustered dispersion.
29. emoji
Counterfactual corpus: imagine doubling the specialist texts most favourable to emoji. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
30. hashtag
Counterfactual corpus: imagine doubling the specialist texts most favourable to hashtag. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and platform-heavy dispersion.
31. doomscrolling
Counterfactual corpus: imagine doubling the specialist texts most favourable to doomscrolling. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and social-media concentrated dispersion.
32. selfie
Counterfactual corpus: imagine doubling the specialist texts most favourable to selfie. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
33. podcast
Counterfactual corpus: imagine doubling the specialist texts most favourable to podcast. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
34. webinar
Counterfactual corpus: imagine doubling the specialist texts most favourable to webinar. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and professional-online contexts dispersion.
35. rubric
Counterfactual corpus: imagine doubling the specialist texts most favourable to rubric. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and education-heavy dispersion.
36. scaffolding
Counterfactual corpus: imagine doubling the specialist texts most favourable to scaffolding. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and sense-clustered dispersion.
37. formative assessment
Counterfactual corpus: imagine doubling the specialist texts most favourable to formative assessment. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
38. hypothesis
Counterfactual corpus: imagine doubling the specialist texts most favourable to hypothesis. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across science/education range and moderate dispersion.
39. methodology
Counterfactual corpus: imagine doubling the specialist texts most favourable to methodology. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in research texts range and register-concentrated dispersion.
40. whereas
Counterfactual corpus: imagine doubling the specialist texts most favourable to whereas. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in formal writing range and register-shaped dispersion.
41. gonna
Counterfactual corpus: imagine doubling the specialist texts most favourable to gonna. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow by mode range and speech-heavy dispersion.
42. kinda
Counterfactual corpus: imagine doubling the specialist texts most favourable to kinda. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow by register range and chat/speech-heavy dispersion.
43. notwithstanding
Counterfactual corpus: imagine doubling the specialist texts most favourable to notwithstanding. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and legal/formal-heavy dispersion.
44. hereby
Counterfactual corpus: imagine doubling the specialist texts most favourable to hereby. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and legal/official concentration dispersion.
45. cheers
Counterfactual corpus: imagine doubling the specialist texts most favourable to cheers. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in some varieties range and region-shaped dispersion.
46. lorry
Counterfactual corpus: imagine doubling the specialist texts most favourable to lorry. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from regionally wide range and geographically uneven dispersion.
47. truck
Counterfactual corpus: imagine doubling the specialist texts most favourable to truck. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and regional variation dispersion.
48. HDB
Counterfactual corpus: imagine doubling the specialist texts most favourable to HDB. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and geographically concentrated dispersion.
49. MRT
Counterfactual corpus: imagine doubling the specialist texts most favourable to MRT. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and geographically concentrated dispersion.
50. NHS
Counterfactual corpus: imagine doubling the specialist texts most favourable to NHS. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and regional/institutional dispersion.
51. SAT
Counterfactual corpus: imagine doubling the specialist texts most favourable to SAT. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and institutional dispersion.
52. AI
Counterfactual corpus: imagine doubling the specialist texts most favourable to AI. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and time-growing dispersion.
53. blockchain
Counterfactual corpus: imagine doubling the specialist texts most favourable to blockchain. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and topic-clustered dispersion.
54. quantum
Counterfactual corpus: imagine doubling the specialist texts most favourable to quantum. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and science-heavy dispersion.
55. inflation
Counterfactual corpus: imagine doubling the specialist texts most favourable to inflation. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide/moderate range and time/topic sensitive dispersion.
56. interest rate
Counterfactual corpus: imagine doubling the specialist texts most favourable to interest rate. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in business/news range and moderate dispersion.
57. biodiversity
Counterfactual corpus: imagine doubling the specialist texts most favourable to biodiversity. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and environment-heavy dispersion.
58. sustainability
Counterfactual corpus: imagine doubling the specialist texts most favourable to sustainability. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and register/domain shaped dispersion.
59. carbon-neutral
Counterfactual corpus: imagine doubling the specialist texts most favourable to carbon-neutral. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and policy/business concentration dispersion.
60. photosphere
Counterfactual corpus: imagine doubling the specialist texts most favourable to photosphere. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from very narrow range and highly concentrated dispersion.
61. the
Counterfactual corpus: imagine doubling the specialist texts most favourable to the. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from very wide range and very even dispersion.
62. people
Counterfactual corpus: imagine doubling the specialist texts most favourable to people. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and fairly even dispersion.
63. time
Counterfactual corpus: imagine doubling the specialist texts most favourable to time. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and fairly even dispersion.
64. change
Counterfactual corpus: imagine doubling the specialist texts most favourable to change. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderately even dispersion.
65. important
Counterfactual corpus: imagine doubling the specialist texts most favourable to important. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderately even dispersion.
66. evidence
Counterfactual corpus: imagine doubling the specialist texts most favourable to evidence. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across formal texts range and moderate dispersion.
67. factor
Counterfactual corpus: imagine doubling the specialist texts most favourable to factor. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across school subjects range and moderate dispersion.
68. model
Counterfactual corpus: imagine doubling the specialist texts most favourable to model. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and uneven by sense dispersion.
69. function
Counterfactual corpus: imagine doubling the specialist texts most favourable to function. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide but domain-shaped range and uneven dispersion.
70. system
Counterfactual corpus: imagine doubling the specialist texts most favourable to system. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
71. algorithm
Counterfactual corpus: imagine doubling the specialist texts most favourable to algorithm. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrower range and technology-heavy dispersion.
72. photosynthesis
Counterfactual corpus: imagine doubling the specialist texts most favourable to photosynthesis. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
73. mitochondria
Counterfactual corpus: imagine doubling the specialist texts most favourable to mitochondria. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
74. jurisdiction
Counterfactual corpus: imagine doubling the specialist texts most favourable to jurisdiction. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and legal concentration dispersion.
75. injunction
Counterfactual corpus: imagine doubling the specialist texts most favourable to injunction. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
76. liquidity
Counterfactual corpus: imagine doubling the specialist texts most favourable to liquidity. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and finance concentration dispersion.
77. basis point
Counterfactual corpus: imagine doubling the specialist texts most favourable to basis point. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
78. latency
Counterfactual corpus: imagine doubling the specialist texts most favourable to latency. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and computing/network-heavy dispersion.
79. bandwidth
Counterfactual corpus: imagine doubling the specialist texts most favourable to bandwidth. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and technology-heavy dispersion.
80. cache
Counterfactual corpus: imagine doubling the specialist texts most favourable to cache. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and technical dispersion.
81. offside
Counterfactual corpus: imagine doubling the specialist texts most favourable to offside. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and sports-heavy dispersion.
82. penalty
Counterfactual corpus: imagine doubling the specialist texts most favourable to penalty. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and sense-dependent dispersion.
83. goal
Counterfactual corpus: imagine doubling the specialist texts most favourable to goal. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
84. tempo
Counterfactual corpus: imagine doubling the specialist texts most favourable to tempo. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and music-heavy dispersion.
85. timbre
Counterfactual corpus: imagine doubling the specialist texts most favourable to timbre. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
86. quarantine
Counterfactual corpus: imagine doubling the specialist texts most favourable to quarantine. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide during crises range and time-variable dispersion.
87. pandemic
Counterfactual corpus: imagine doubling the specialist texts most favourable to pandemic. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from broad in crisis periods range and time-clustered dispersion.
88. lockdown
Counterfactual corpus: imagine doubling the specialist texts most favourable to lockdown. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from broad in 2020-era texts range and time-clustered dispersion.
89. emoji
Counterfactual corpus: imagine doubling the specialist texts most favourable to emoji. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
90. hashtag
Counterfactual corpus: imagine doubling the specialist texts most favourable to hashtag. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and platform-heavy dispersion.
91. doomscrolling
Counterfactual corpus: imagine doubling the specialist texts most favourable to doomscrolling. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and social-media concentrated dispersion.
92. selfie
Counterfactual corpus: imagine doubling the specialist texts most favourable to selfie. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
93. podcast
Counterfactual corpus: imagine doubling the specialist texts most favourable to podcast. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and moderate dispersion.
94. webinar
Counterfactual corpus: imagine doubling the specialist texts most favourable to webinar. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and professional-online contexts dispersion.
95. rubric
Counterfactual corpus: imagine doubling the specialist texts most favourable to rubric. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow/moderate range and education-heavy dispersion.
96. scaffolding
Counterfactual corpus: imagine doubling the specialist texts most favourable to scaffolding. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and sense-clustered dispersion.
97. formative assessment
Counterfactual corpus: imagine doubling the specialist texts most favourable to formative assessment. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and highly concentrated dispersion.
98. hypothesis
Counterfactual corpus: imagine doubling the specialist texts most favourable to hypothesis. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide across science/education range and moderate dispersion.
99. methodology
Counterfactual corpus: imagine doubling the specialist texts most favourable to methodology. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in research texts range and register-concentrated dispersion.
100. whereas
Counterfactual corpus: imagine doubling the specialist texts most favourable to whereas. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in formal writing range and register-shaped dispersion.
101. gonna
Counterfactual corpus: imagine doubling the specialist texts most favourable to gonna. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow by mode range and speech-heavy dispersion.
102. kinda
Counterfactual corpus: imagine doubling the specialist texts most favourable to kinda. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow by register range and chat/speech-heavy dispersion.
103. notwithstanding
Counterfactual corpus: imagine doubling the specialist texts most favourable to notwithstanding. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and legal/formal-heavy dispersion.
104. hereby
Counterfactual corpus: imagine doubling the specialist texts most favourable to hereby. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow range and legal/official concentration dispersion.
105. cheers
Counterfactual corpus: imagine doubling the specialist texts most favourable to cheers. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide in some varieties range and region-shaped dispersion.
106. lorry
Counterfactual corpus: imagine doubling the specialist texts most favourable to lorry. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from regionally wide range and geographically uneven dispersion.
107. truck
Counterfactual corpus: imagine doubling the specialist texts most favourable to truck. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and regional variation dispersion.
108. HDB
Counterfactual corpus: imagine doubling the specialist texts most favourable to HDB. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and geographically concentrated dispersion.
109. MRT
Counterfactual corpus: imagine doubling the specialist texts most favourable to MRT. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and geographically concentrated dispersion.
110. NHS
Counterfactual corpus: imagine doubling the specialist texts most favourable to NHS. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and regional/institutional dispersion.
111. SAT
Counterfactual corpus: imagine doubling the specialist texts most favourable to SAT. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from narrow globally range and institutional dispersion.
112. AI
Counterfactual corpus: imagine doubling the specialist texts most favourable to AI. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide range and time-growing dispersion.
113. blockchain
Counterfactual corpus: imagine doubling the specialist texts most favourable to blockchain. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and topic-clustered dispersion.
114. quantum
Counterfactual corpus: imagine doubling the specialist texts most favourable to quantum. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from moderate range and science-heavy dispersion.
115. inflation
Counterfactual corpus: imagine doubling the specialist texts most favourable to inflation. Frequency rises, but ask whether range and dispersion improve. This separates corpus composition effects from genuinely broad language use.
Selection verdict: state which list—general, academic, technical, regional or event-specific—would best represent the item and justify the decision from wide/moderate range and time/topic sensitive dispersion.