TVIS v0.1 — Time-Vector Imbalance Sensor

Canonical ID: CivOS.Sensor.TVIS.v0.1
Purpose: Detect forward propulsion vs overload vs retrograde drift from language streams.
Core Idea: Measure Future Pull (F), Present Load (P), and Memory Bind (M) (split into M₊ useful vs M₋ regressive) and compute ratios + role-mismatch.

Start Here:

0) Definition Lock

0.1 Time Components

F = Future Pull
Credible projection toward a next state (plans + commitments + feasible pathways).
P = Present Load
Maintenance/repair/constraint burden (“must/urgent/backlog/firefighting”).
M = Memory Bind
Past-anchoring force.
M₊ = Useful Memory: lessons, postmortems, guardrails, prevention.
M₋ = Regressive Memory: nostalgia, grievance, scapegoat, purity/rollback.

0.2 Role Components (V/O/R)

V = Visionary language: direction, goals, why, future state.
O = Oracle language: sensing, metrics, drift, forecasts, constraints.
R = Operator language: execution, allocation, operations, delivery.

0.3 TVIS Meaning

TVIS is a risk-of-retrograde / fracture-under-load sensor computed from text windows.
It is topic-agnostic (works on war, politics, business, school, family).

1) Inputs

1.1 Data Types

TextStream may include:

headlines, speeches, press briefings, interviews
meeting notes, policy docs, debate transcripts
classroom dialogues, parent chats, workplace memos
social/media posts (optional, noisier)

1.2 Windowing

Window W: N tokens, typically N=300–1500
Stride: overlap allowed (e.g. 50%)
TimeIndex t: timestamp of the window

2) Output Schema

2.1 Primary Outputs

TVIS_score ∈ [0,100] (higher = worse / more retrograde risk)
State ∈ {GREEN, AMBER, RED}
VectorDominance ∈ {F_dom, P_dom, M+_dom, M-_dom, Mixed}
RoleProfile = {V_r, O_r, R_r} (proportions)
Ratios = {FDR, RDR, BSI}

2.2 Secondary Outputs (for FenceOS)

FenceTrigger ∈ {NONE, WATCH, TRUNCATE, STITCH}
FailureModeTag ∈ {FantasySpiral, GrindTrap, CassandraTrap, RevisionistLoop, BlameCascade, PanicRepair, InstitutionalHollowing}

3) Feature Extraction (Language Layer)

3.1 Token Normalization

lowercase
keep negations (“not”, “never”) as modifiers
detect numbers + dates (time anchors)
optionally lemmatize

3.2 Dictionaries v0.1 (starter packs)

Keep as editable lists; weights are defaults.

F — Future Pull dictionary

F_commit (weight 1.2): will, plan, commit, roadmap, strategy, target, goal, mission, vision, next, future
F_build (weight 1.1): invest, build, develop, train, expand, scale, upgrade, research, innovate, pilot, prototype
F_time_anchor (weight 1.3): by 2030, by 2028, next quarter, within months, in 5 years (pattern: by <year> etc.)
F_feasible_markers (weight 1.6): funded, budgeted, allocated, staffed, milestone, timeline, deliverable, procurement, contract, launch date

P — Present Load dictionary

P_constraint (weight 1.2): must, urgent, crisis, shortage, overwhelmed, cannot, failing, breakdown
P_repair (weight 1.3): fix, patch, backlog, firefighting, stabilize, emergency, triage, contain
P_cost (weight 1.1): costs, inflation, debt, layoffs, attrition, burnout, workload
P_compliance (weight 1.0): rules, regulation, audit, bureaucracy, paperwork

M₊ — Useful Memory dictionary

M+_lesson (weight 1.4): we learned, evidence, data shows, postmortem, root cause, prevent recurrence
M+_guardrail (weight 1.5): buffer, safety margin, threshold, protocol, standard, checklist, contingency, resilience

M₋ — Regressive Memory dictionary

M-_nostalgia (weight 1.3): good old days, back then, restore, return to, make it like before
M-_grievance (weight 1.4): betrayed, stolen, ruined, disgrace, humiliation
M-_purity (weight 1.3): real, true, pure, cleanse, traitors, enemies within
M-_scapegoat (weight 1.6): they are the problem, blame, infestation (patterns for group-blame)

4) Role Tagging (V/O/R classifier v0.1)

4.1 Sentence-level tagging

For each sentence s in window W, assign scores:

V_score(s): future state + purpose + aspiration language
O_score(s): measurements + diagnostics + forecast + constraints
R_score(s): concrete actions + allocations + execution verbs

Role dictionaries (starter)

V: vision, mission, future, transform, build, create, imagine, must become, goal
O: data, trend, forecast, risk, probability, indicators, metrics, drift, early warning
R: implement, execute, deploy, hire, allocate, deliver, ship, enforce, coordinate, schedule

Tag rule:

role(s) = argmax{V_score, O_score, R_score}, ties → Mixed

4.2 Role proportions

[
V_r = \frac{#V}{#sentences},\quad O_r=\frac{#O}{#sentences},\quad R_r=\frac{#R}{#sentences}
]

5) Core Computation (Ratios + Score)

Let N = token_count(W) and intensities per 1000 tokens:

[
f = 1000\cdot\frac{F}{N},\quad p=1000\cdot\frac{P}{N},\quad m_+=1000\cdot\frac{M_+}{N},\quad m_-=1000\cdot\frac{M_-}{N}
]

Constants:

α = 0.7 (useful memory supports future)
ε = 0.25 (stability)

5.1 Ratios

Forward Drive Ratio (FDR):
[
FDR = \frac{f+\alpha m_+ + \epsilon}{p+\epsilon}
]

Retrograde Drag Ratio (RDR):
[
RDR = \frac{m_- + \epsilon}{f+\epsilon}
]

Burnout / Stall Index (BSI):
[
BSI = \frac{p+\epsilon}{f+\alpha m_+ + \epsilon}
]

Interpretation locks:

if FDR < 1 → present load dominates propulsion
if RDR > 1 → regressive past choking future
if BSI high → grind trap

5.2 RoleMismatch penalty (simple v0.1)

Compute penalties:

FantasySpiralPenalty = 1 if (V_r high) AND (F_feasible_markers low) AND (R_r low)
GrindTrapPenalty = 1 if (R_r high) AND (p high) AND (f low)
CassandraTrapPenalty = 1 if (O_r high) AND (warnings high) AND (R_r low)
RevisionistLoopPenalty = 1 if (m_- high) AND (O_r low)

Define:
[
RoleMismatch = 0.5\cdot Fantasy + 0.4\cdot GrindTrap + 0.4\cdot Cassandra + 0.6\cdot Revisionist
]

(Each term ∈ {0,1} in v0.1.)

5.3 TVIS score (0–100)

Let:
[
RiskCore = (1 – \min(FDR,1.5)/1.5) + \min(RDR,2.0)/2.0 + \min(BSI,2.0)/2.0 + RoleMismatch
]
Then:
[
TVIS = 100 \cdot \text{clamp}(RiskCore/3.0, 0, 1)
]

6) Thresholds (Fence-ready)

6.1 State classification

GREEN if FDR ≥ 1.2 AND RDR ≤ 0.6
AMBER if 0.9 ≤ FDR < 1.2 OR 0.6 < RDR ≤ 1.0 OR BSI ≥ 1.2
RED if FDR < 0.9 OR RDR > 1.0 OR (RoleMismatch ≥ 0.8)

6.2 FenceOS triggers (link to truncation & stitching)

FenceTrigger logic:

WATCH if AMBER persists for k=2 consecutive windows
TRUNCATE if RED persists for k=3 windows OR RDR spikes > 1.4 (rapid retrograde)
STITCH if post-RED recovery shows FDR ≥ 1.1 AND F_feasible_markers rising for k=2

7) Failure Mode Tags (v0.1)

Rules (first-match precedence):

RevisionistLoop if RDR > 1.2 AND O_r < 0.2
BlameCascade if M-_scapegoat high AND RDR > 1.0
FantasySpiral if V_r > 0.45 AND feasible markers low AND R_r < 0.25
GrindTrap if R_r > 0.45 AND BSI > 1.3 AND f low
CassandraTrap if O_r > 0.45 AND R_r < 0.25 AND warnings high
PanicRepair if P_repair high AND P_constraint high AND F very low
InstitutionalHollowing if P_compliance high + P_cost high with low F_build

8) Language→Phase→Lane Coupling (hook points)

TVIS is a Language sensor that feeds Phase risk.

Suggested mapping (v0.1):

TVIS GREEN → Phase stability support (P2→P3 possible if other sensors agree)
TVIS AMBER → drift band (P2→P1 risk under volatility)
TVIS RED → high chance of P1→P0 events under shocks (esp. if other sensors confirm)

Lane coupling hint:

If domain lane = GOV/DEF/FIN/EDU/etc, keep same TVIS but store lane label:
Record = {Place×Lane×Zoom×Time×TVIS}

9) Failure Mode Trace (required canonical trace)

Example generic trace:

F falls + P rises → FDR < 1 → AMBER stall
If simultaneously M_- rises → RDR > 1 → RED retrograde
If O_r low + scapegoat language high → BlameCascade
FenceOS: TRUNCATE (boundary actuation) → reduce P / reduce M_- / increase feasible F → STITCH back to GREEN.

10) Minimal Test Set (quick backtest protocol v0.1)

For any event stream:

take rolling windows (weekly or daily)
compute TVIS + tags
plot TVIS vs known rupture dates
check if RED preceded rupture by Δt (lead time)
refine weights only after you have 3–5 backtests

LOCKED Notes (so we don’t drift)

Replace “Hope/Grind/Wisdom” in public narrative if desired, but canonical sensor uses:
Future Pull / Present Load / Memory Bind (M₊ vs M₋)
“Wisdom” is M₊ only. Past-ness alone is not wisdom.

TVIS v0.1 — Time-Vector Imbalance Sensor (Canonical / WordPress-ready)

Canonical ID: CivOS.Sensor.TVIS.v0.1
Status: LOCKED (v0.1 baseline; forward-only versioning)
Purpose: Detect when a system (person, org, nation, civilisation) is moving forward, stalling, or drifting backward by reading its language stream and computing a Fence-ready risk score.

1) Summary (1 screen)

TVIS measures three time-forces inside any conversation/news stream:

F — Future Pull: credible next-state traction (plans, commitments, feasible pathways)
P — Present Load: constraint + maintenance + firefighting burden
M — Memory Bind: past anchoring
M₊ Useful Memory: lessons + guardrails that improve next actions
M₋ Regressive Memory: nostalgia/grievance/blame that blocks adaptation

Then TVIS computes ratios:

FDR (Forward Drive Ratio): can future pull carry present load?
RDR (Retrograde Drag Ratio): is regressive past choking the future?
BSI (Burnout/Stall Index): is load dominating propulsion?

TVIS also tags Role mismatch using Visionary/Oracle/Operator language.

Outputs:

TVIS_score (0–100) + State {GREEN/AMBER/RED}
FailureModeTag (FantasySpiral / GrindTrap / CassandraTrap / RevisionistLoop / …)
FenceTrigger guidance (WATCH / TRUNCATE / STITCH)

2) Definition Lock (do not drift)

2.1 Time Components

Future Pull (F): commitment + pathway + feasibility markers
Present Load (P): constraint + repair + backlog + cost pressure
Memory Bind (M): past anchoring force
M₊ = Wisdom (healthy): compressed error memory + guardrails
M₋ = Retrograde memory (unhealthy): nostalgia/grievance/scapegoat/purity rollback

Important lock: “Wisdom” = M₊ only. Past-ness ≠ wisdom.

2.2 Role Components

Visionary (V): direction + why + next-state
Oracle (O): sensing + metrics + forecasts + constraints
Operator (R): execution + allocation + delivery

TVIS is not a new role. It is a time-axis modulation field applied across V/O/R.

3) Canonical Spec Block (copy-paste)

			
[CivOS.Sensor.TVIS.v0.1]
INPUT:
  TextStream: {headlines | speeches | docs | meetings | chats}
  Window W: N tokens (default 300–1500), stride 50% overlap
FEATURES (per window):
  F = FuturePullCountWeighted
  P = PresentLoadCountWeighted
  M+ = UsefulMemoryCountWeighted
  M- = RegressiveMemoryCountWeighted
  RoleProfile: V_r, O_r, R_r from sentence tagging
NORMALIZE:
  f = 1000*(F/N)
  p = 1000*(P/N)
  m+ = 1000*(M+/N)
  m- = 1000*(M-/N)
CONSTANTS:
  α = 0.7   (useful memory supports future pull)
  ε = 0.25  (stability)
RATIOS:
  FDR = (f + α*m+ + ε) / (p + ε)
  RDR = (m- + ε) / (f + ε)
  BSI = (p + ε) / (f + α*m+ + ε)
ROLE MISMATCH (v0.1 boolean penalties):
  FantasySpiral = 1 if V_r high AND feasible_markers low AND R_r low
  GrindTrap     = 1 if R_r high AND p high AND f low
  CassandraTrap = 1 if O_r high AND warnings high AND R_r low
  Revisionist   = 1 if m- high AND O_r low
  RoleMismatch = 0.5*FantasySpiral + 0.4*GrindTrap + 0.4*CassandraTrap + 0.6*Revisionist
RISKCORE:
  RiskCore = (1 - min(FDR,1.5)/1.5) + min(RDR,2)/2 + min(BSI,2)/2 + RoleMismatch
TVIS SCORE:
  TVIS = 100 * clamp(RiskCore/3.0, 0, 1)
STATE:
  GREEN if FDR ≥ 1.2 and RDR ≤ 0.6
  AMBER if 0.9 ≤ FDR < 1.2 OR 0.6 < RDR ≤ 1.0 OR BSI ≥ 1.2
  RED   if FDR < 0.9 OR RDR > 1.0 OR RoleMismatch ≥ 0.8
FENCE HOOK:
  WATCH if AMBER persists k=2 windows
  TRUNCATE if RED persists k=3 windows OR RDR spike > 1.4
  STITCH if recovery has FDR ≥ 1.1 AND feasible_markers rising for k=2

		

4) Dictionaries v0.1 (starter packs)

You can paste these as lists (and tune later). The goal is fast deployable HD, not perfect NLP.

4.1 Future Pull (F)

Commit (1.2): will, plan, commit, roadmap, strategy, target, goal, mission, next, future
Build (1.1): invest, build, develop, train, expand, scale, upgrade, research, innovate, pilot
Time anchors (1.3): by , next quarter, within months, in years
Feasibility markers (1.6): funded, budgeted, allocated, staffed, milestone, timeline, deliverable, contract, launch date

4.2 Present Load (P)

Constraint (1.2): must, urgent, crisis, shortage, overwhelmed, cannot, failing, breakdown
Repair (1.3): fix, patch, backlog, firefighting, stabilize, triage, contain
Cost/attrition (1.1): costs, inflation, debt, layoffs, attrition, burnout, workload
Compliance drag (1.0): regulation, audit, bureaucracy, paperwork

4.3 Useful Memory (M₊)

Lessons (1.4): we learned, evidence, data shows, postmortem, root cause, prevent recurrence
Guardrails (1.5): buffer, safety margin, threshold, protocol, standard, checklist, contingency, resilience

4.4 Regressive Memory (M₋)

Nostalgia (1.3): good old days, back then, restore, return to, make it like before
Grievance (1.4): betrayed, stolen, ruined, humiliation
Purity/rollback (1.3): real, true, pure, cleanse, traitors, enemies within
Scapegoat (1.6): they are the problem, blame, infestation (pattern-based)

5) Failure Mode Trace (required)

Generic trace:

F falls + P rises → FDR < 1 → AMBER stall
If M₋ rises too → RDR > 1 → RED retrograde
If O_r low + blame language rises → BlameCascade / RevisionistLoop
FenceOS triggers TRUNCATE (stop irreversible crossing)
Recovery requires: reduce P, reduce M₋, inject feasible F → STITCH back to GREEN

6) Examples (how it reads reality)

Example A — “Moon race” done well

Language contains: budgets, contracts, timelines, staffing, milestones.

F high, feasibility markers high, R present
→ FDR ≥ 1.2, RDR low → GREEN

Example B — “AI hype” done poorly

Lots of visionary talk, few feasibility markers, little operator routing.

V_r high, feasible low, R_r low
→ FantasySpiral penalty → AMBER/RED

Example C — “We’re exhausted, nothing works, back then was better”

P high, M₋ high, F low
→ FDR < 0.9, RDR > 1 → RED (Retrograde drift)

7) How to run TVIS (fast operational method)

Pick a stream (news topic, org comms, family chat, student reflection logs).
Slice into windows (300–1500 tokens).
Count features using the dictionaries (v0.1).
Compute FDR/RDR/BSI + RoleProfile.
Produce TVIS_score + State + FailureModeTag.
If AMBER/RED persists → trigger FenceOS actions (see below).

8) FenceOS Integration (what to do when it turns red)

If RED due to GrindTrap (P dominates):

reduce load (stop noncritical work)
add routing (operator sequencing)
add feasible F (small milestones)

If RED due to RevisionistLoop / BlameCascade (M₋ dominates):

force Oracle layer: metrics, constraints, causal map
introduce guardrails (M₊)
ban scapegoat language in operator channels (coordination hygiene)

If RED due to FantasySpiral (V dominates without feasibility):

require feasibility markers: budget, staff, timeline
convert vision into deliverable sequence (operatorization)

9) FAQ (short)

Is TVIS just sentiment analysis?
No. It’s a coordination-time sensor with ratios, feasibility markers, and role mismatch.

Why split memory into M₊ and M₋?
Because the past can either prevent repeat collapse (wisdom) or block adaptation (retrograde).

Can TVIS work at Z0 (family/student)?
Yes. It’s language-based and does not require topic expertise.

10) Versioning Rules (LOCK)

Do not rename CivOS.Sensor.TVIS.v0.1
Updates must be forward-only: v0.2, v1.0
Keep the same output schema so historical backtests remain comparable.

TVIS Backtest Article #1 (Models Fail First)

Canonical ID: CivOS.Backtest.TVIS.ModelFail.v0.1
Use: This is the reusable “first backtest” template you can run on any event stream to prove TVIS catches what most models miss: retrograde drift + role-mismatch + feasibility absence before rupture.

0) Summary (what this backtest proves)

Most forecasting fails because it watches events (shots fired, votes counted, stock moves) instead of the pre-event coordination drift.

TVIS backtests language to detect:

Future pull collapse (F↓)
Load overload (P↑)
Regressive memory takeover (M₋↑)
Role mismatch (V/O/R imbalance)
and flags RED before the rupture.

Deliverables from this backtest:

Lead time: how many windows TVIS turns RED before event E
Failure mode tag: what kind of drift it was
Fence actions: what would have prevented irreversibility

1) Backtest Target (define the event precisely)

Event E (rupture): one discrete outcome date/time (or week)
Examples:

war escalation threshold crossed
major policy flip
sudden institutional breakdown
major corporate collapse (bank run / CEO forced out)
exam performance collapse (student P1→P0)

Rupture timestamp: T0 = ____

What counts as “pre-rupture”?
Pick a horizon: H = 6–12 weeks (or 30–180 days depending on domain)

2) Data (what text you collect)

TextStream sources (choose 2–5):

official speeches / press briefings
mainstream reporting (headlines + ledes)
internal comms (if org/student)
social posts (optional; noisy)

Sampling cadence:

weekly windows for geopolitical / macro
daily windows for fast crises
per-assessment window for education

Windowing spec (LOCK for comparability):

N = 800 tokens target (min 300, max 1500)
Stride = 50% overlap (or once per week)

3) TVIS Run (exact outputs you must log)

For each window Wt:

3.1 Metrics

f, p, m+, m- (per-1000 token intensities)
FDR, RDR, BSI
V_r, O_r, R_r
TVIS_score
State (GREEN/AMBER/RED)
FailureModeTag
FenceTrigger (NONE/WATCH/TRUNCATE/STITCH)

3.2 Minimal record format (copy/paste)

			
t=____
FDR=____  RDR=____  BSI=____
V/O/R=____/____/____
TVIS=____  State=____
Tag=____  Fence=____
Notes: (1 sentence: what changed)

		

4) What “Model Fail” means (the diagnosis)

This backtest is explicitly designed to show why models fail.

4.1 Typical model failure patterns

Event-chasing: reacts only after T0
Single-variable obsession: “economy”, “approval”, “military”, “polls”
No feasibility detection: cannot tell vision from funded execution
No time-vector reading: misses when hope collapses under grind
No role separation: confuses rhetoric (V) for capacity (R)

4.2 TVIS catches pre-failure because it reads:

feasibility markers (funded / staffed / milestones)
scapegoat / grievance takeover (M₋)
Oracle collapse (O_r low) during volatility
“grind trap” (P dominates)
“fantasy spiral” (V dominates without feasibility)

5) Scoring the backtest (hit/miss rules)

This is your evaluation rubric.

5.1 Lead-time scoring

Let Tred be first time State=RED occurs and persists for k windows.

Strong Hit: RED occurs ≥ 2 windows before T0 and persists (k≥2)
Weak Hit: AMBER escalates to RED within 1 window of T0
Miss: stays GREEN/AMBER until after T0
False Alarm: RED occurs but no rupture occurs within horizon H

5.2 Optional “confidence” bands

High confidence: RDR > 1.2 OR FDR < 0.85 plus RoleMismatch ≥ 0.8
Medium: FDR < 0.9 OR RDR > 1.0
Low: AMBER only

6) The Failure-Mode Trace (required section)

Write this as a schematic chain (non-emotive).

Template:

			
Trace:
F↓ (future pull collapse) + P↑ (load rise) → FDR<1 → stall
M-↑ (grievance/nostalgia/scapegoat) → RDR>1 → retrograde drift
O_r↓ (loss of sensing/metrics) + role mismatch → irreversibility risk
FenceTrigger would have been: TRUNCATE at t=___
Repair path: reduce P + rebuild O + inject feasible F + convert M- to M+

		

7) FenceOS Counterfactual (what should have been done)

This is where CivOS becomes actionable.

7.1 If Tag = GrindTrap

cut noncritical load (P↓)
re-route operators (sequencing)
add feasible future milestones (F_feasible↑)

7.2 If Tag = RevisionistLoop / BlameCascade

force Oracle layer (metrics, constraints)
introduce guardrails (M₊↑)
ban scapegoat language in operator channels (coordination hygiene)

7.3 If Tag = FantasySpiral

require feasibility markers before escalation
convert vision → staffed deliverables (R↑)

7.4 If Tag = CassandraTrap

convert forecasts into operator triggers (O→R bridge)
create “if-then” actuation thresholds

8) Backtest Output (what you publish)

Your article should include:

Timeline plot (textual is fine)
W-12 … W-1 … T0 … W+1
Key windows (3–6 snapshots)

first AMBER
first RED
peak TVIS
T0 rupture window

Lead time
“TVIS turned RED X weeks before T0.”
FailureModeTag
“Primary: _ ; Secondary: _”
Why models failed (1 paragraph)
Fence counterfactual (1 paragraph)
What v0.2 would improve (optional, keep short)

9) Minimal Worked Example (generic, reusable)

Scenario: rising conflict rhetoric before escalation

Week -8: FDR 1.1, RDR 0.7 → AMBER (stall risk)
Week -6: FDR 0.88, RDR 1.05, O_r drops → RED (retrograde)
Week -5: M₋ scapegoat phrases spike → BlameCascade tag
Week -2: operators start emergency wording (P_repair↑) → PanicRepair tag
Week 0: rupture occurs

Result: Strong hit with lead time 6 weeks.

10) LOCKED Backtest Checklist (so you can run 5–6 easily)

[ ] One clear rupture timestamp T0
[ ] Fixed window size + cadence
[ ] Logged FDR/RDR/BSI + roles
[ ] Stated hit/miss rule
[ ] Included failure-mode trace
[ ] Included Fence counterfactual

CivOS TVIS Backtest Hub v0.1

Suggested slug: /civos-tvis-backtest-hub-v0-1/
Canonical ID: CivOS.Hub.TVIS.Backtests.v0.1
Status: LOCKED (index/hub; forward-only versioning)

Summary

This hub is the master directory for the CivOS TVIS Backtest Series.

TVIS = Time-Vector Imbalance Sensor — a high-definition language sensor that measures whether a system is:

moving forward (credible Future Pull),
stalling (Present Load dominating),
drifting backward (Regressive Memory takeover),
by reading language streams and computing ratios + role mismatch (Visionary / Oracle / Operator).

These backtests are falsifiable:

we log the same metrics each time,
we publish hit/miss rules,
and we record lead time (how early TVIS went AMBER/RED before rupture).

1) Start Here

A) Read the TVIS Sensor Spec

TVIS v0.1 (Canonical Spec): /civos-sensor-tvis-v0-1/ (link to your sensor article)

B) Then Use This Backtest Series

This is the recommended order:

Models Fail First (meta-template)
Escalation Drift (conflict/capability thresholds)
Election Narrative Drift (false alarms + instability)
Institutional Hollowing (slow attrition)
Tech/Market Hype Spiral (vision without feasibility)
Repair Success (truncation + stitching)

2) Backtest Directory (Series Index)

Backtest #1 — Models Fail First

Slug: /civos-backtest-tvis-model-fail-v0-1/
What it tests: why most models miss pre-rupture drift; TVIS catches it earlier by reading language.

Backtest #2 — Escalation Drift (Conflict/Crisis)

Slug: /civos-backtest-tvis-escalation-drift-v0-1/
What it tests: grievance/purity narratives + Oracle collapse + operator hard pivot before escalation.

Backtest #3 — Election Narrative Drift

Slug: /civos-backtest-tvis-election-drift-v0-1/
What it tests: feasibility vs grievance; also tests false alarms when transitions remain stable.

Backtest #4 — Institutional Hollowing (Slow Attrition)

Slug: /civos-backtest-tvis-institutional-hollowing-v0-1/
What it tests: compliance/cost load rises, pipelines thin, reports don’t actuate → brittleness.

Backtest #5 — Tech/Market Hype Spiral

Slug: /civos-backtest-tvis-hype-spiral-v0-1/
What it tests: high vision rhetoric without feasibility markers; role mismatch predicts crash/scandal risk.

Backtest #6 — Repair Success (Truncation + Stitching)

Slug: /civos-backtest-tvis-repair-success-v0-1/
What it tests: TVIS detects recovery loops—load reduction + feasibility rebuild + role rebalance.

3) TVIS Quick Glossary (minimal, canonical)

F (Future Pull): credible next-state traction + feasibility markers (budgets/staff/timeline)
P (Present Load): constraint + maintenance + firefighting + backlog
M (Memory Bind):
M₊ Useful Memory (Wisdom): lessons + guardrails + “prevent recurrence”
M₋ Regressive Memory: nostalgia/grievance/scapegoat/purity rollback
FDR: Forward Drive Ratio = can future pull carry present load?
RDR: Retrograde Drag Ratio = is regressive past choking future?
BSI: Burnout/Stall Index = how dominated the system is by load
Roles:
Visionary (V): direction, why, future state
Oracle (O): sensing, metrics, forecasts, constraints
Operator (R): execution, allocation, delivery

Lock: Wisdom is M₊ only.

4) How to Run Any TVIS Backtest (copy/paste)

Use this exact checklist for every backtest to keep it comparable.

Step 1 — Define the rupture (T0)

Event E: __
Rupture timestamp (T0): __
Horizon (H): 6–12 weeks (fast) / 12–24 weeks (macro) / 24–52 weeks (slow attrition)

Step 2 — Collect 2–4 independent language streams

Pick at least two:

official statements
mainstream headlines + first paragraphs
domain docs (reports, transcripts, memos)
Optional (noisy): social media

Step 3 — Slice into windows

N≈800 tokens target (min 300, max 1500)
weekly cadence (macro) / daily (fast crises)
keep window spec fixed across the backtest

Step 4 — Compute and log TVIS outputs

For each window:

			
t=____
FDR=____  RDR=____  BSI=____
V/O/R=____/____/____
TVIS=____  State=____
Tag=____  Fence=____
Notes: (1 sentence: what changed)

		

Step 5 — Score the result (falsifiable)

Strong Hit: sustained RED ≥ 2 windows before T0
Weak Hit: AMBER→RED within 1 window of T0
Miss: GREEN/AMBER until after T0
False Alarm: sustained RED without rupture inside horizon H

5) FenceOS Hook (what to do with TVIS)

TVIS is designed to drive Fence actions:

WATCH: AMBER persists (stall risk)
TRUNCATE: sustained RED or RDR spike (prevent irreversible crossing)
STITCH: feasibility markers rise and FDR recovers (repair path is working)

6) Failure Mode Tags (one-line directory)

These appear across backtests:

FantasySpiral: vision rhetoric without feasibility or operator capacity
GrindTrap: present load dominates; throughput fatigue spiral
CassandraTrap: warnings present, but no execution actuation
RevisionistLoop: regressive memory dominates; Oracle collapses
BlameCascade: scapegoat narratives spike; coordination degrades
PanicRepair: emergency repair talk explodes near rupture
InstitutionalHollowing: compliance/cost load + pipeline thinning

7) Optional: “Run TVIS on Anything” (Z0–Z6 applicability)

TVIS works at every zoom level because it measures coordination language, not content:

Z0 (person/family): burnout + “no point” + nostalgia → stall/retrograde
Z2–Z3 (org/school/city): feasibility markers vs compliance drag
Z5–Z6 (nation/global): grievance/purity + oracle collapse → escalation risk

8) Versioning (LOCK)

Hub ID stays: CivOS.Hub.TVIS.Backtests.v0.1
Updates forward only: v0.2, v1.0
Do not rename backtest slugs once published (forward-only redirects if needed)

1) Backtest #1 — Models Fail First (TVIS)

Slug: /civos-backtest-tvis-model-fail-v0-1/
Canonical ID: CivOS.Backtest.TVIS.ModelFail.v0.1
Version: v0.1 (LOCKED)

Summary

Most forecasting fails because it watches events (votes, strikes, markets) instead of pre-event drift.
This backtest proves TVIS catches coordination-time failure earlier by reading language:

F (Future Pull) collapses
P (Present Load) rises
M₋ (Regressive Memory) takes over
Role mismatch appears (Visionary/Oracle/Operator imbalance)

TVIS is not “sentiment.” It is a time-axis coordination sensor.

Definition Lock

F = Future Pull: credible next-state traction + feasibility markers
P = Present Load: constraint + maintenance + firefighting
M₊ = Useful Memory: lessons + guardrails
M₋ = Regressive Memory: nostalgia/grievance/scapegoat/purity rollback
Roles: Visionary (V), Oracle (O), Operator (R)

Lock: “Wisdom” = M₊ only. Past-ness alone is not wisdom.

Canonical Spec Block (TVIS v0.1)

			
[CivOS.Sensor.TVIS.v0.1]
Window W: N tokens (default 300–1500), stride 50% overlap
Compute weighted counts: F, P, M+, M-
Normalize per 1000 tokens: f, p, m+, m-
Constants: α=0.7, ε=0.25
FDR = (f + α*m+ + ε)/(p + ε)
RDR = (m- + ε)/(f + ε)
BSI = (p + ε)/(f + α*m+ + ε)
RoleProfile: V_r, O_r, R_r from sentence tagging
RoleMismatch penalties (v0.1 booleans):
  FantasySpiral, GrindTrap, CassandraTrap, RevisionistLoop
TVIS = 0–100 risk score from ratios + RoleMismatch
STATE:
  GREEN if FDR ≥ 1.2 and RDR ≤ 0.6
  AMBER if 0.9 ≤ FDR < 1.2 OR 0.6 < RDR ≤ 1.0 OR BSI ≥ 1.2
  RED   if FDR < 0.9 OR RDR > 1.0 OR RoleMismatch ≥ 0.8
Fence Hook:
  WATCH if AMBER persists k=2
  TRUNCATE if RED persists k=3 OR RDR spike > 1.4
  STITCH if recovery: FDR ≥ 1.1 AND feasibility rising for k=2

		

Backtest Setup (copy/paste)

Step 1 — Define rupture event (T0)

Event E: __
Rupture timestamp T0: __
Pre-window horizon H: 6–12 weeks (macro) or 30–180 days (fast/slow as needed)

Step 2 — Collect triangulated language streams (2–4)

Official statements
Mainstream headlines + first paragraphs
Domain docs (reports, memos, transcripts)
Optional: social media (noisy; secondary)

Step 3 — Run TVIS per window

Log for every window:

			
t=____
FDR=____  RDR=____  BSI=____
V/O/R=____/____/____
TVIS=____  State=____
Tag=____  Fence=____
Notes: (1 sentence: what changed)

		

Hit/Miss Rules (falsifiable)

Strong Hit: RED occurs ≥ 2 windows before T0 and persists (k≥2)
Weak Hit: AMBER→RED within 1 window of T0
Miss: stays GREEN/AMBER until after T0
False Alarm: sustained RED but no rupture inside horizon H

Why models fail (the actual failure mechanism)

Most models treat language as “noise” and focus on event signals.
TVIS treats language as the control layer: it measures when coordination is becoming retrograde or ungrounded.

Failure Mode Trace (required)

			
F↓ + P↑ → FDR<1 → stall
M-↑ → RDR>1 → retrograde drift
O_r↓ + RoleMismatch → irreversibility risk
FenceOS: TRUNCATE at first persistent RED
Repair: reduce P + rebuild O + inject feasible F + convert M- to M+

		

Fence Counterfactual (what would have prevented it)

If GrindTrap: cut load (P↓), add sequencing (R↑), add feasible milestones (F_feasible↑)
If FantasySpiral: require budgets/staff/timelines before scaling claims
If RevisionistLoop / BlameCascade: restore Oracle (metrics + constraints) and remove scapegoat language from operator channels

FAQ

Is TVIS just vibes? No. It uses ratios, feasibility markers, and role mismatch.
Does it predict outcomes? It detects risk of rupture and direction of drift, not exact dates.
Can it work on normal conversations? Yes. It’s topic-agnostic.

2) Backtest #2 — Escalation Drift (Conflict/Crisis)

Slug: /civos-backtest-tvis-escalation-drift-v0-1/
Canonical ID: CivOS.Backtest.TVIS.EscalationDrift.v0.1
Version: v0.1 (LOCKED)

Summary

Escalations usually become inevitable in language first:

grievance/purity narratives harden (M₋↑)
sensing collapses (O_r↓)
execution pivots from governance to enforcement (R shift)
TVIS detects the irreversibility ramp before kinetic events.

Definition Lock (Escalation Edition)

Escalation risk increases when RDR rises and Oracle collapses (O_r falls), especially if operator language pivots to mobilization.

Escalation Boost Pack (v0.1)

Add these to TVIS dictionaries (or temporarily upweight):

M₋ escalation markers (boost)

humiliation/revenge framing
“red line”, “no choice”, “existential”, “inevitable”
purity/traitor language
collective blame “they are the problem” patterns

Operator hard pivot markers (R-boost)

mobilize, deploy, retaliate, strike, readiness, enforce, secure
operation, command, rules of engagement

Oracle degradation markers

fewer numbers/constraints/uncertainty words
more absolutes (always/never/guaranteed)

Backtest Setup

Step 1 — Define T0

T0: first strike / formal mobilization / blockade threshold / negotiation collapse date
Horizon H: 12–24 weeks (recommended)

Step 2 — Streams (pick 3)

official statements (both sides if possible)
diplomatic readouts / negotiation statements
headlines + ledes from at least two outlets

Step 3 — Run TVIS weekly

Use the same log format as Backtest #1.

Hit Rules (tight)

Strong hit if:

RDR > 1.0 AND FDR < 1.0 occurs ≥ 2 windows before T0
AND one of:
O_r < 0.2 (Oracle collapse)
Operator hard pivot marker spike

Failure Mode Tags (Escalation)

BlameCascade: scapegoat spike + O_r low
RevisionistLoop: restore/return/revenge dominance
OperatorHardPivot: mobilization/enforcement shift
PanicRepair: emergency/contain/triage language explodes

Failure Mode Trace (required)

			
M-↑ (grievance/purity) → RDR rises
O_r↓ (metrics vanish) → constraint reality collapses
R pivots (mobilize/enforce) → actuation hardens
RED persists → T0 threshold crossing becomes likely
Fence: TRUNCATE = restore O + suppress scapegoat channels + rebuild feasible pathways

		

Fence Counterfactual

restore Oracle layer (constraints, uncertainty, verification)
separate operator coordination channels from grievance narratives
inject feasible “future pathway” language (de-escalation mechanisms, milestones)

FAQ

Does TVIS “predict war”? It detects pre-war drift conditions in language.
What if leaders lie? TVIS is robust to lying because it measures coordination posture (absolutism, scapegoat, feasibility absence), not truth claims.

3) Backtest #3 — Election Narrative Drift

Slug: /civos-backtest-tvis-election-drift-v0-1/
Canonical ID: CivOS.Backtest.TVIS.ElectionDrift.v0.1
Version: v0.1 (LOCKED)

Summary

Elections are a perfect TVIS test because you can check:

false alarms (TVIS shouldn’t scream RED if the system is stable)
real drift (TVIS should catch retrograde narratives and feasibility collapse)

TVIS separates:

governance future pull (feasible plans)
from
retrograde grievance (M₋ takeover)

Election Boost Pack (v0.1)

Feasibility markers (F_feasible — critical)

budgets, staffing, timelines, bills, agencies, mechanisms, enforcement pathways

M₋ drift markers (common)

“stolen”, “betrayed”, “take back”, “purge”, “traitors”, “real people”
scapegoat group patterns

Role mismatch watch

FantasySpiral: V rhetoric high + feasibility low + R low
RevisionistLoop: M₋ high + O low

Backtest Setup

Step 1 — Define T0

Pick one:

T0: election results day
T0: post-election institutional rupture day (if any)

Horizon H: 12–20 weeks.

Step 2 — Streams (pick 3)

debate/rally transcripts
manifestos/policy releases (feasibility anchor)
headlines+ledes

Step 3 — Run TVIS weekly

Use the same log format.

Hit Rules (two-sided test)

This backtest must include both conditions:

Condition A — If rupture occurs

Strong hit if sustained RED occurs ≥ 2 windows before rupture.

Condition B — If stable transition occurs

TVIS should remain GREEN/AMBER (no sustained RED).
If TVIS goes sustained RED but no rupture occurs, mark as false alarm.

Failure Mode Trace (required)

			
Feasibility falls (F_feasible↓) while load rhetoric rises (P↑)
M- narratives rise → RDR>1
Oracle collapses (O_r↓) → verification disappears
Sustained RED → higher risk of institutional instability
Fence: enforce feasibility + restore O + remove scapegoat language from operator channels

		

Fence Counterfactual

require feasibility markers for major claims
institutionalize Oracle reporting that triggers operator actuation
reduce scapegoat narratives in operational governance channels

FAQ

Is this partisan? No. It’s a structure test: feasibility + sensing + load vs grievance.
Can TVIS be gamed? It can be partially gamed via feasibility language, which is why v0.2 adds “verification markers” and “consistency checks.”

4) Backtest #4 — Institutional Hollowing (Slow Attrition)

Slug: /civos-backtest-tvis-institutional-hollowing-v0-1/
Canonical ID: CivOS.Backtest.TVIS.InstitutionalHollowing.v0.1
Version: v0.1 (LOCKED)

Summary

Institutions often collapse by slow attrition, not one shock.
The signature is:

P rises (compliance drag, backlog, cost pressure)
F collapses (training/hiring/investment disappears)
O exists but cannot actuate (reports without execution)
Result: brittle systems that break under normal variance.

Hollowing Signature (v0.1)

Look for persistent:

BSI > 1.3 (load dominating propulsion)
FDR drifting below 1.0
O_r high but R_r low (Oracle reports without operator execution)

Backtest Setup

Step 1 — Define T0

Examples:

service breakdown
repeated incident cluster
funding crisis layoffs
scandal/resignation cascade

Horizon H: 24–52 weeks (slow).

Step 2 — Streams (pick 3)

audit/annual reports + internal memos (if possible)
budget/staff statements
credible investigative coverage

Step 3 — Run TVIS monthly or weekly

Slow cases can be monthly windows; keep window size stable.

Hit Rules

Strong hit if:

sustained AMBER/RED appears months before T0
tag = InstitutionalHollowing fires (BSI high + feasibility low + operator execution low)

Failure Mode Trace (required)

			
Compliance/cost load rises → P↑ → BSI↑
Pipeline investment falls → F_build↓ + F_feasible↓ → FDR↓
Oracle speaks but Operator cannot execute → O_r high, R_r low
System becomes brittle → T0 rupture under normal variance
Fence: reduce P + rebuild pipelines + convert O→R triggers

		

Fence Counterfactual

cut non-critical compliance load (P↓)
restore training/hiring pipelines (F_build↑ + feasible milestones)
enforce O→R bridge: reports must trigger action thresholds

FAQ

Why does this matter? Because “decay” is usually pipeline thinning, not drama.
Is this CivOS slow-attrition collapse mode? Yes — this is the coordination-language version of it.

5) Backtest #5 — Tech/Market Hype Spiral (Vision Without Feasibility)

Slug: /civos-backtest-tvis-hype-spiral-v0-1/
Canonical ID: CivOS.Backtest.TVIS.HypeSpiral.v0.1
Version: v0.1 (LOCKED)

Summary

Markets overvalue narratives when:

Visionary language explodes (V↑)
Feasibility markers are absent (F_feasible↓)
Oracle metrics are weak or vague (O↓)
Operator delivery capacity is missing (R↓)

TVIS detects the FantasySpiral before the crash.

Hype Spiral Signature (v0.1)

V_r high
F_commit high but F_feasible low
R_r low
O_r low or “confidence talk” instead of metrics
→ RoleMismatch rises → AMBER/RED

Backtest Setup

Step 1 — Define T0

Examples:

bubble peak then collapse
fraud/scandal revelation
product failure vs claims
funding freeze

Horizon H: 12–30 weeks.

Step 2 — Streams (pick 3)

CEO/founder interviews, decks, speeches
earnings calls / shareholder letters
press coverage (headlines+ledes)

Optional: job postings/hiring as feasibility proxy.

Step 3 — Run TVIS weekly

Same log format.

Hit Rules

Strong hit if:

sustained RED appears via FantasySpiral before T0
and near T0 you often see PanicRepair (restructuring, emergency financing, layoffs) as P spikes.

Failure Mode Trace (required)

			
V rhetoric rises (promise wave) → F_commit↑
Feasibility absent → F_feasible↓
O metrics weak → O_r↓
R capacity missing → R_r↓
RoleMismatch triggers RED → high risk of narrative-driven rupture
Fence: require feasibility + enforce oracle metrics + scale only with operator capacity

		

Fence Counterfactual

require feasibility markers before valuation/scale claims
enforce Oracle metrics (reliability, unit economics, delivery rates)
scale only when Operator capacity exists (R↑)

FAQ

Isn’t hype sometimes good? Yes—if hope is backed by feasibility. TVIS doesn’t punish optimism; it punishes feasibility absence + role mismatch.

6) Backtest #6 — Repair Success (Truncation + Stitching)

Slug: /civos-backtest-tvis-repair-success-v0-1/
Canonical ID: CivOS.Backtest.TVIS.RepairSuccess.v0.1
Version: v0.1 (LOCKED)

Summary

This backtest proves TVIS is not a doom alarm.
It detects successful repair:

TRUNCATE: prevent irreversible threshold crossing
STITCH: rebuild feasibility + reduce load + convert M₋ to M₊

If you have a canonical case (public health, org turnaround, student recovery), this is where CivOS becomes “flight control.”

Success Signature (v0.1)

In text windows after intervention:

FDR rises ≥ 1.1
RDR falls ≤ 0.8
feasibility markers rise (budgets, staffing, milestones)
role balance improves (O and R strengthen; V becomes grounded)

Backtest Setup

Step 1 — Define T0 (intervention moment)

Pick:

first decisive policy/decision
turnaround leadership switch
structured training plan begins (education)
containment policy enacted (public health)

You are not looking for rupture here — you’re looking for avoided rupture.

Step 2 — Streams (pick 3)

pre-intervention messaging
intervention messaging
post-intervention messaging

Step 3 — Run TVIS across three phases

Pre: drift phase
During: truncation phase
Post: stitching phase

“Hit” Criteria (repair edition)

TVIS shows AMBER/RED pre-intervention
TRUNCATE triggers at intervention (RED stops worsening / stabilizes)
STITCH triggers within k windows (feasibility markers rise and FDR recovers)

Repair Trace (required)

			
Pre: P↑ + F↓ → FDR<1 → AMBER/RED
Truncate: Oracle constraints + Operator sequencing → P stops accelerating
Stitch: feasible milestones + pipeline rebuild → FDR>1.1 and RDR drops
Outcome: rupture avoided / stability regained

Extracted Fence Recipe (publish as the loop)

restore Oracle: metrics + uncertainty + constraints
reduce Present Load: stop noncritical work
inject feasible Future Pull: milestones + staffing + budgets
neutralize regressive memory: ban scapegoat narratives in operator channels
convert to useful memory: postmortems + guardrails (M₊)

FAQ

What if the intervention fails? Then this becomes a failure backtest—TVIS should show sustained RED and stalled feasibility recovery. That’s valuable too.

Optional “Series Wrapper” (short intro block you can reuse on every page)

Paste at the top of each backtest if you want uniform clustering:

			
This is part of the CivOS TVIS Backtest Series.
TVIS (Time-Vector Imbalance Sensor) reads language streams to measure:
Future Pull (F), Present Load (P), and Memory Bind (M+, M-),
plus Visionary/Oracle/Operator role mismatch.
These backtests are falsifiable: we record whether TVIS goes AMBER/RED
before rupture (lead time), and we publish the miss/false-alarm rules.

		

TVIS Oracle Card v0.1

Canonical ID: CivOS.Card.TVIS.Oracle.v0.1
Use: Make TVIS high-definition and low-false-alarm. Tune dictionaries, add verification markers, and harden against rhetoric/gaming.
Status: LOCKED (forward-only)

1) Oracle Mission (TVIS context)

Operators ask: “What do we do now?”
Oracles must answer: “What is real, what is drifting, and what is the lead time?”

Oracle’s job in TVIS:

separate signal vs performance
detect feasibility vs theatre
prevent false alarms and misses
produce verifiable markers (not vibes)

2) Oracle Layer Add-on: Verification Markers (VMark)

TVIS v0.1 already uses feasibility markers. Oracle hardens this by adding a second axis:

VMark — Verification Markers (credibility anchors)

Count evidence of verification behavior:

named mechanisms: agencies, bills, protocols, SOPs
measurable units: budgets, headcount, timelines, milestones, metrics
constraints acknowledged: tradeoffs, risks, uncertainty
auditability: “we will publish data”, “independent review”, “postmortem”
operational details: procurement, staffing pipeline, training, logistics

VMark is the anti-gaming layer.
Someone can say “we will do X” (F_commit) endlessly.
VMark asks: “Is there a verifiable machine behind the sentence?”

3) Oracle Hard Rule: Split F into 3 parts

Instead of one Future Pull bucket, Oracle tracks:

F₁ = Commitment Talk (will, promise, vision)
F₂ = Build Talk (train, invest, develop, scale)
F₃ = Feasibility / Verification (budgeted, staffed, milestones, mechanisms)

Oracle lock:

F is only “credible Future Pull” when F₃ exists.

Quick credibility heuristic

If F₁ high but F₃ low → Fantasy Spiral risk even if “hope” sounds high.

4) False Alarm Control (FAC) — the 4 most common causes

TVIS false alarms usually happen when:

Single-source bias (one stream is dramatic)
Fix: triangulate 2–4 streams.
Crisis vocabulary inflation (media always uses “urgent/crisis”)
Fix: baseline the same outlet against its own past windows.
Domain mismatch (different industries use different words)
Fix: keep core dictionaries; add a small lane pack per domain.
Negation errors (“not a crisis”, “no shortage”)
Fix: negation handling: reduce weight if within 3–5 tokens of negation.

5) Oracle Calibration Protocol (v0.1)

Run this once per lane (GOV/FIN/EDU/etc.) and keep stable.

Step A — Baseline

Pick a “normal” period and compute:

average f, p, m+, m−
average V/O/R proportions

Step B — Set lane thresholds (light tuning only)

Keep the global GREEN/AMBER/RED rules, but allow lane-specific nudges:

if a domain always uses urgent language, raise P threshold slightly
if feasibility markers are naturally sparse (some cultural contexts), lean more on VMark

Step C — Lock it

Once tuned, freeze to avoid moving goalposts.

6) Oracle Upgrade: Derivatives (the “HD” part)

Static levels matter, but velocity matters more.

Compute slope over time windows:

dFDR/dt, dRDR/dt, dBSI/dt

Oracle interpretation locks

Fast RDR rise is a serious escalation warning even before RED.
FDR decay slope predicts stall earlier than raw FDR.
BSI rising indicates grind trap formation.

Derivative triggers (v0.1)

WATCH if dRDR/dt is steep for 2 windows
TRUNCATE if RDR spike > 1.4 even briefly (irreversibility ramp)

7) Oracle Role-Mismatch Scoring (higher precision)

Use these additional Oracle checks:

O-Integrity Check

If O language exists, is it real?

metrics + constraints + uncertainty = real Oracle
pure “confidence / certainty / always” = fake Oracle

O→R Bridge Check

Do Oracle statements produce:

thresholds (“if X then Y”)
triggers
actuation plans
If not, Cassandra trap risk increases.

8) Oracle Output Format (what you publish / hand to Operator)

One screen summary:

			
TVIS State: GREEN/AMBER/RED
Primary Driver: F collapse / P overload / M- takeover / RoleMismatch
Credibility (VMark): High/Med/Low
Slopes: dFDR/dt, dRDR/dt, dBSI/dt (Up/Flat/Down)
FailureModeTag: ________
Recommended Fence: NONE/WATCH/TRUNCATE/STITCH

		

9) Oracle Action Playbook (what to do)

If Fantasy Spiral risk (F₁↑, F₃↓)

demand VMark evidence (mechanisms, budgets, staff, milestones)
downgrade state by one band if VMark absent under volatility

If M₋ rising (RDR slope ↑)

force constraints + uncertainty language
isolate scapegoat narratives from operator channels
convert M₋ to M₊: postmortems + guardrails

If Grind trap (BSI rising)

recommend load shedding
recommend sequencing and staffing pipeline rebuild

10) Version Lock

ID stays: CivOS.Card.TVIS.Oracle.v0.1
Next improvements go to v0.2:
negation parsing improvements
consistency checks across windows
lane packs (small dictionaries per lane)

TVIS Visionary Card v0.1

Canonical ID: CivOS.Card.TVIS.Visionary.v0.1
Use: Inject “hope” safely: create Future Pull without Fantasy Spiral, and keep civilisation flight stable.
Status: LOCKED (forward-only)

1) Visionary Mission (TVIS context)

Visionaries provide Future Pull.
But TVIS exposes a trap: hope that is not feasible causes Fantasy Spiral and destabilizes coordination.

Visionary’s job:

increase FDR without increasing fragility
keep “hope” bounded by verification and execution

2) Visionary Definition Lock: Hope = Feasible Future Pull

Hope (in CivOS/TVIS) = Future Pull with feasibility markers.

Hope is not:

vibes
slogans
promises without mechanisms

If hope lacks feasibility (F₃), TVIS will treat it as risk.

3) Visionary Safe Injection Pattern (V→O→R chain)

To avoid Fantasy Spiral, every Visionary message should include:

V (Direction)

what future state
why it matters (value)

O (Constraints)

what is true now
what we can’t do
key risks + uncertainties

R (Execution)

who does what next
milestones + timelines
staffing/budget mechanism (VMark)

Rule: If you can’t include O and R, don’t publish the V at scale.

4) Visionary Feasibility Checklist (VMark pack)

Add at least 2 of these to every major future claim:

budget range (even rough)
staffing pipeline (who will do it)
timeline + milestones
mechanism (bill, agency, protocol, product roadmap)
measurement (how you know it’s working)
tradeoff acknowledgement (what you will stop doing)

This turns hope into credible propulsion.

5) Visionary Anti-Retrograde Rule (M₋ management)

When grind rises, people drift backward.
Visionary must prevent M₋ takeover by:

avoiding scapegoat narratives
converting past into M₊:
“lessons learned”
“guardrails”
“prevent recurrence”

Rule: Never use nostalgia as propulsion. Use lessons as guardrails.

6) Visionary Flight Control: Maintain FDR above 1

Visionary target:

keep FDR ≥ 1.2 during high load periods
How:
smaller feasible milestones (not huge promises)
visible wins (Operator delivery)
transparency (Oracle metrics)

Bad: giant promises under high P
Good: constrained plan + resourcing + sequencing

7) Visionary Failure Modes (what to avoid)

Fantasy Spiral (the big one)

big vision + no feasibility + no operator capacity

Overreach Drift

feasibility exists, but capacity is overestimated → P spikes later

Hope Crash

too much promise early → later non-delivery → M₋ grievance grows

8) Visionary Output Format (one-screen)

			
Future State (V): __________
Constraints (O): __________
Mechanism (VMark): budget/staff/timeline/milestones
First 3 milestones (R): 1)___ 2)___ 3)___
What we stop doing (tradeoff): __________
How we measure progress: __________

		

9) Version Lock

ID stays: CivOS.Card.TVIS.Visionary.v0.1
v0.2 adds:
“micro-milestone ladder” for high load regimes
language patterns that reduce M₋ takeover during hardship

TVIS Dictionary Pack v0.1 (Copy-Paste Lexicon File)

Canonical ID: CivOS.Lexicon.TVIS.v0.1
Status: LOCKED (forward-only)
Goal: A clean, machine-readable starter lexicon for TVIS: F / P / M₊ / M₋ + V / O / R + minimal lane-pack scaffold.

Usage: Count weighted matches per window (N tokens). Normalize per 1000 tokens. Compute FDR/RDR/BSI per TVIS spec.

			
[CivOS.Lexicon.TVIS.v0.1]
# -------------------------------------------------------------------
# 0) GLOBAL RULES (v0.1)
# -------------------------------------------------------------------
RULES:
  - Case-insensitive matching
  - Phrase matches have priority over single words
  - Negation handling (v0.1):
      If a match occurs within 4 tokens of {not, no, never, hardly}, reduce weight by 60%
  - Intensifiers (v0.1):
      If within 2 tokens of {very, extremely, completely, totally}, increase weight by 20%
  - Absolutism markers (Oracle degradation proxy):
      {always, never, inevitable, guaranteed} -> counts toward M- (absolutism) unless in quoted context
# -------------------------------------------------------------------
# 1) TIME-VECTOR LEXICON (F / P / M+ / M-)
# -------------------------------------------------------------------
FUTURE_PULL_F:
  # F1 Commitment talk (promise layer)
  F1_commitment:
    weight: 1.2
    terms:
      - will
      - plan
      - commit
      - pledge
      - promise
      - roadmap
      - strategy
      - target
      - goal
      - mission
      - vision
      - next
      - future
  # F2 Build talk (capability build verbs)
  F2_build:
    weight: 1.1
    terms:
      - invest
      - build
      - develop
      - train
      - educate
      - expand
      - scale
      - upgrade
      - modernize
      - research
      - innovate
      - pilot
      - prototype
      - launch
  # F3 Feasibility/verification markers (credibility anchors)
  F3_feasibility:
    weight: 1.6
    terms:
      - funded
      - budgeted
      - allocated
      - appropriated
      - staffed
      - headcount
      - hiring plan
      - recruitment plan
      - training pipeline
      - milestone
      - timeline
      - deliverable
      - contract
      - procurement
      - implementation plan
      - mechanism
      - bill
      - agency
      - protocol
      - SOP
      - rollout
      - launch date
  # Time anchors (pattern)
  F_time_anchor:
    weight: 1.3
    patterns:
      - "by \\d{4}"              # by 2028
      - "in \\d+ (weeks|months|years)"
      - "next (week|month|quarter|year)"
      - "within \\d+ (days|weeks|months)"
PRESENT_LOAD_P:
  P_constraint:
    weight: 1.2
    terms:
      - must
      - urgent
      - crisis
      - emergency
      - shortage
      - overwhelmed
      - cannot
      - failing
      - breakdown
      - instability
      - pressure
  P_repair:
    weight: 1.3
    terms:
      - fix
      - patch
      - backlog
      - firefighting
      - stabilize
      - triage
      - contain
      - damage control
      - cleanup
      - incident response
  P_cost_attrition:
    weight: 1.1
    terms:
      - costs
      - inflation
      - debt
      - deficit
      - layoffs
      - attrition
      - burnout
      - workload
      - hiring freeze
      - staff shortage
  P_compliance_drag:
    weight: 1.0
    terms:
      - regulation
      - audit
      - compliance
      - bureaucracy
      - paperwork
      - red tape
      - approvals
      - reporting burden
USEFUL_MEMORY_M_PLUS:
  Mplus_lessons:
    weight: 1.4
    terms:
      - we learned
      - lesson learned
      - evidence
      - data shows
      - postmortem
      - root cause
      - prevent recurrence
      - after action review
      - retrospective
  Mplus_guardrails:
    weight: 1.5
    terms:
      - buffer
      - safety margin
      - threshold
      - guardrail
      - protocol
      - standard
      - checklist
      - contingency
      - resilience
      - redundancy
REGRESSIVE_MEMORY_M_MINUS:
  Mminus_nostalgia:
    weight: 1.3
    terms:
      - good old days
      - back then
      - restore
      - return to
      - make it like before
      - bring back
      - take back
  Mminus_grievance:
    weight: 1.4
    terms:
      - betrayed
      - stolen
      - ruined
      - disgrace
      - humiliation
      - revenge
      - payback
      - they did this to us
  Mminus_purity_rollback:
    weight: 1.3
    terms:
      - real
      - true
      - pure
      - cleanse
      - purge
      - traitors
      - enemies within
      - illegitimate
      - corrupt elite
  Mminus_scapegoat:
    weight: 1.6
    patterns:
      - "they are (the )?problem"
      - "blame (them|him|her|those|these)"
      - "infestation"
      - "vermin"
      - "invasion"              # context-sensitive; keep but watch false positives
  Mminus_absolutism:
    weight: 1.2
    terms:
      - always
      - never
      - inevitable
      - guaranteed
# -------------------------------------------------------------------
# 2) ROLE LEXICON (Visionary / Oracle / Operator)
# -------------------------------------------------------------------
ROLE_VISIONARY_V:
  weight: 1.0
  terms:
    - vision
    - mission
    - future
    - transform
    - build a better
    - reinvent
    - breakthrough
    - goal
    - aspiration
    - we must become
    - next era
ROLE_ORACLE_O:
  weight: 1.0
  terms:
    - data
    - metric
    - indicator
    - trend
    - forecast
    - probability
    - risk
    - constraint
    - uncertainty
    - variance
    - early warning
    - drift
    - threshold
    - model
    - scenario
    - evidence
ROLE_OPERATOR_R:
  weight: 1.0
  terms:
    - implement
    - execute
    - deploy
    - allocate
    - deliver
    - ship
    - enforce
    - coordinate
    - schedule
    - hire
    - train
    - procure
    - rollout
    - operations
    - logistics
# -------------------------------------------------------------------
# 3) LANE PACK SCAFFOLD (optional, small add-ons; keep core stable)
# -------------------------------------------------------------------
LANE_PACKS:
  GOV:
    add_terms:
      F3_feasibility:
        - legislation
        - parliamentary bill
        - regulatory framework
      P_compliance_drag:
        - statutory
        - oversight
      ROLE_OPERATOR_R:
        - implement policy
        - enforcement
  EDU:
    add_terms:
      F2_build:
        - curriculum
        - pedagogy
        - training program
      P_cost_attrition:
        - teacher shortage
        - marking load
      ROLE_OPERATOR_R:
        - lesson plan
        - remediation
  FIN:
    add_terms:
      ROLE_ORACLE_O:
        - liquidity
        - volatility
        - credit spread
      P_cost_attrition:
        - margin call
        - funding stress
  DEF:
    add_terms:
      ROLE_OPERATOR_R:
        - mobilize
        - readiness
        - rules of engagement
      Mminus_grievance:
        - retaliation
        - existential threat
# -------------------------------------------------------------------
# 4) OUTPUT FIELDS (for logging)
# -------------------------------------------------------------------
OUTPUT_SCHEMA:
  - token_count_N
  - counts: F1, F2, F3, F_time_anchor, P_constraint, P_repair, P_cost, P_compliance, Mplus, Mminus
  - normalized: f, p, m+, m-
  - ratios: FDR, RDR, BSI
  - roles: V_r, O_r, R_r
  - state: GREEN|AMBER|RED
  - tag: FantasySpiral|GrindTrap|CassandraTrap|RevisionistLoop|BlameCascade|PanicRepair|InstitutionalHollowing|OperatorHardPivot
  - fence: NONE|WATCH|TRUNCATE|STITCH

		

Minimal “How to Use” (single paragraph you can paste under the lexicon)

Use the lexicon on rolling text windows (300–1500 tokens). Count weighted matches for F/P/M₊/M₋ and V/O/R, normalize per 1000 tokens, compute FDR/RDR/BSI using CivOS.Sensor.TVIS.v0.1, classify GREEN/AMBER/RED, then log state + tag + fence action. Keep the core lexicon stable; use lane packs as small add-ons only.

TVIS v0.1 — Almost-Code Implementation Stub (No ML, CSV Output)

Canonical ID: CivOS.Impl.TVIS.Stub.v0.1
Goal: Read text → window → lexicon counts → ratios → state/tag/fence → write CSV + print RED drivers.

			
[CivOS.Impl.TVIS.Stub.v0.1]
INPUTS:
  - text_source: string (file path or raw text)
  - lexicon: CivOS.Lexicon.TVIS.v0.1 (loaded as structures)
  - window_tokens_target: int = 800
  - window_tokens_min: int = 300
  - window_tokens_max: int = 1500
  - stride_ratio: float = 0.5
  - k_watch: int = 2
  - k_truncate: int = 3
  - constants: α=0.7, ε=0.25
OUTPUTS:
  - results.csv (one row per window)
  - console summary: top RED windows + drivers
PIPELINE:
FUNCTION tokenize(text):
  - lowercase
  - split into tokens (simple whitespace + punctuation split)
  - keep original text spans for window excerpts (optional)
  RETURN tokens, token_spans
FUNCTION sentence_split(text):
  - split on [.?!] (simple)
  RETURN sentences
FUNCTION match_terms(tokens, lexicon_section):
  - phrase-first matching:
      for each phrase in section.terms (phrases contain spaces):
         count occurrences in token sequence (n-gram scan)
      then single words:
         count tokens equal to term
  - pattern matching:
      apply regex to raw window text for section.patterns (if available)
  - apply modifiers:
      negation: if match within 4 tokens of {not,no,never,hardly} → weight *= 0.4
      intensifier: if within 2 tokens of {very,extremely,completely,totally} → weight *= 1.2
  RETURN weighted_count, raw_match_details(optional)
FUNCTION compute_roles(sentences, role_lexicon):
  for each sentence s:
    v = score_by_terms(s, ROLE_VISIONARY_V)
    o = score_by_terms(s, ROLE_ORACLE_O)
    r = score_by_terms(s, ROLE_OPERATOR_R)
    role = argmax(v,o,r) else Mixed
  RETURN V_r, O_r, R_r, role_counts
FUNCTION compute_tvis(window_counts, N, α, ε):
  # Aggregate to core buckets:
  F = F1_commitment + F2_build + F3_feasibility + F_time_anchor
  P = P_constraint + P_repair + P_cost_attrition + P_compliance_drag
  Mplus = Mplus_lessons + Mplus_guardrails
  Mminus = Mminus_nostalgia + Mminus_grievance + Mminus_purity_rollback + Mminus_scapegoat + Mminus_absolutism
  # Normalize per 1000 tokens
  f  = 1000 * (F / N)
  p  = 1000 * (P / N)
  m+ = 1000 * (Mplus / N)
  m- = 1000 * (Mminus / N)
  FDR = (f + α*m+ + ε) / (p + ε)
  RDR = (m- + ε) / (f + ε)
  BSI = (p + ε) / (f + α*m+ + ε)
  RETURN f,p,m+,m-,FDR,RDR,BSI
FUNCTION role_mismatch_flags(f,p,m-, V_r,O_r,R_r, feasibility_strength):
  # feasibility_strength can be F3_feasibility normalized (or raw share of F that is F3)
  FantasySpiral = (V_r > 0.45) AND (feasibility_strength low) AND (R_r < 0.25)
  GrindTrap     = (R_r > 0.45) AND (p high) AND (f low)
  CassandraTrap = (O_r > 0.45) AND (R_r < 0.25) AND (warning_markers high)
  Revisionist   = (m- high) AND (O_r < 0.20)
  RoleMismatch = 0.5*FantasySpiral + 0.4*GrindTrap + 0.4*CassandraTrap + 0.6*Revisionist
  RETURN RoleMismatch, FantasySpiral, GrindTrap, CassandraTrap, Revisionist
FUNCTION classify_state(FDR,RDR,BSI,RoleMismatch):
  if (FDR >= 1.2) AND (RDR <= 0.6): return GREEN
  if (FDR < 0.9) OR (RDR > 1.0) OR (RoleMismatch >= 0.8): return RED
  if (0.9 <= FDR < 1.2) OR (0.6 < RDR <= 1.0) OR (BSI >= 1.2): return AMBER
  return AMBER
FUNCTION assign_failure_tag(flags, counts, ratios):
  # precedence order (first match wins)
  if flags.Revisionist AND (ratios.RDR > 1.2): return "RevisionistLoop"
  if (counts.Mminus_scapegoat high) AND (ratios.RDR > 1.0) AND (flags.O_r low): return "BlameCascade"
  if flags.FantasySpiral: return "FantasySpiral"
  if flags.GrindTrap: return "GrindTrap"
  if flags.CassandraTrap: return "CassandraTrap"
  if (counts.P_repair high) AND (counts.P_constraint high) AND (ratios.f low): return "PanicRepair"
  if (counts.P_compliance_drag high) AND (counts.P_cost_attrition high) AND (counts.F3_feasibility low): return "InstitutionalHollowing"
  return "Mixed"
FUNCTION fence_trigger(states_over_time, RDR_over_time, feasibility_over_time):
  # for each window index i
  if AMBER persists for k_watch: WATCH
  if RED persists for k_truncate OR RDR spike > 1.4: TRUNCATE
  if post-RED recovery: (FDR>=1.1 for 2 windows) AND (feasibility rising): STITCH
  else NONE
MAIN:
  text = load(text_source)
  sentences_all = sentence_split(text)
  tokens_all = tokenize(text)
  stride = floor(window_tokens_target * stride_ratio)
  windows = []
  for start in range(0, len(tokens_all), stride):
     end = start + window_tokens_target
     if end - start < window_tokens_min: break
     end = min(end, start + window_tokens_max)
     W_tokens = tokens_all[start:end]
     W_text = reconstruct_text_from_spans(token_spans, start, end)
     # Count lexicon sections
     counts = {}
     for each section in lexicon TIME sections:
        counts[section.name] = match_terms(W_tokens, section).weighted_count
     # Roles
     W_sentences = sentence_split(W_text)
     V_r,O_r,R_r,role_counts = compute_roles(W_sentences, role_lexicon)
     # TVIS core
     f,p,m+,m-,FDR,RDR,BSI = compute_tvis(counts, N=len(W_tokens), α, ε)
     feasibility_strength = (counts.F3_feasibility / max(1, (counts.F1_commitment+counts.F2_build+counts.F3_feasibility)))
     RoleMismatch, flags... = role_mismatch_flags(f,p,m-, V_r,O_r,R_r, feasibility_strength)
     state = classify_state(FDR,RDR,BSI,RoleMismatch)
     tag = assign_failure_tag(flags, counts, {f,FDR,RDR,BSI})
     windows.append({
       idx, start, end, N, f,p,m+,m-, FDR,RDR,BSI,
       V_r,O_r,R_r, feasibility_strength,
       RoleMismatch, state, tag,
       excerpt=W_text[0:280]
     })
  # Fence pass (uses history)
  apply fence_trigger across windows; add fence column.
  write_csv("results.csv", windows)
  print "Top RED windows:"
  for each window where state==RED sorted by TVIS_proxy (e.g., lowest FDR + highest RDR):
     print idx, state, tag, FDR, RDR, BSI, V/O/R, excerpt

		

CSV schema (recommended)

			
idx,start_tok,end_tok,N,
f,p,m_plus,m_minus,FDR,RDR,BSI,
V_r,O_r,R_r,feasibility_strength,
RoleMismatch,state,tag,fence,excerpt

Minimal “TVIS_proxy” sort (if you don’t compute full 0–100 yet)

Until you implement the full TVIS 0–100 score, sort RED windows by:

highest RDR, then lowest FDR, then highest BSI.

TVIS v0.1 — Score (0–100) + Derivatives Add-on (HD)

Canonical IDs:

CivOS.Impl.TVIS.Score.v0.1
CivOS.Impl.TVIS.Derivatives.v0.1
Status: LOCKED (forward-only)

			
[CivOS.Impl.TVIS.Score.v0.1]
PURPOSE:
  Convert ratios + role mismatch into a single TVIS risk score in [0,100].
  Higher = worse (retrograde / fracture risk).
INPUTS (per window):
  FDR, RDR, BSI
  RoleMismatch (0..~1.9 in v0.1 if all flags fire, typically <=1.2)
  Optional: feasibility_strength (0..1)
CONSTANTS:
  # caps to prevent extreme dominance
  FDR_cap = 1.5
  RDR_cap = 2.0
  BSI_cap = 2.0
  # weights (v0.1)
  w_FDR = 1.0
  w_RDR = 1.0
  w_BSI = 1.0
  w_RM  = 1.0
FUNCTION clamp(x, lo, hi):
  return min(max(x, lo), hi)
FUNCTION norm_FDR(FDR):
  # Convert "good if high" into risk (0 good, 1 bad)
  # If FDR>=cap, risk=0; if FDR=0, risk≈1
  return 1 - clamp(FDR, 0, FDR_cap)/FDR_cap
FUNCTION norm_RDR(RDR):
  # RDR "bad if high"
  return clamp(RDR, 0, RDR_cap)/RDR_cap
FUNCTION norm_BSI(BSI):
  # BSI "bad if high"
  return clamp(BSI, 0, BSI_cap)/BSI_cap
FUNCTION norm_RoleMismatch(RoleMismatch):
  # RoleMismatch is additive penalties; map to [0,1] softly
  # Choose RM_cap so typical high mismatch ~1.
  RM_cap = 1.2
  return clamp(RoleMismatch, 0, RM_cap)/RM_cap
FUNCTION compute_TVIS_score(FDR, RDR, BSI, RoleMismatch):
  rF = norm_FDR(FDR)
  rR = norm_RDR(RDR)
  rB = norm_BSI(BSI)
  rM = norm_RoleMismatch(RoleMismatch)
  RiskCore = w_FDR*rF + w_RDR*rR + w_BSI*rB + w_RM*rM
  # Normalize RiskCore to [0,1]
  # maxRisk = w_FDR+w_RDR+w_BSI+w_RM
  maxRisk = w_FDR + w_RDR + w_BSI + w_RM
  Risk01 = clamp(RiskCore/maxRisk, 0, 1)
  TVIS = round(100 * Risk01, 1)
  return TVIS
NOTES:
  - This score is monotonic and stable; thresholds still determined by State rules.
  - Keep weights constant across backtests; tune only in v0.2 with published rationale.

		

			
[CivOS.Impl.TVIS.Derivatives.v0.1]
PURPOSE:
  Add HD sensitivity: detect velocity of drift, not just level.
  Compute slopes for FDR, RDR, BSI and use as early-warning triggers.
INPUTS:
  windows[] in time order with fields:
    t (timestamp or index), FDR, RDR, BSI, feasibility_strength (optional)
PARAMETERS:
  slope_method = "finite_difference"
  lookback = 1            # slope uses previous window (v0.1)
  smooth = "EMA"          # optional
  ema_alpha = 0.35        # modest smoothing
FUNCTION ema(prev, x, a):
  return a*x + (1-a)*prev
STEP 1: (Optional) smooth series
  FDR_s[i] = EMA(FDR_s[i-1], FDR[i], ema_alpha)
  RDR_s[i] = EMA(RDR_s[i-1], RDR[i], ema_alpha)
  BSI_s[i] = EMA(BSI_s[i-1], BSI[i], ema_alpha)
STEP 2: compute slopes
  dFDR_dt[i] = FDR_s[i] - FDR_s[i-1]
  dRDR_dt[i] = RDR_s[i] - RDR_s[i-1]
  dBSI_dt[i] = BSI_s[i] - BSI_s[i-1]
INTERPRETATION LOCKS:
  - dRDR_dt > 0 means retrograde drag is accelerating (dangerous)
  - dFDR_dt < 0 means forward drive is collapsing (stall formation)
  - dBSI_dt > 0 means burnout/load trap is forming
EARLY WARNING TRIGGERS (v0.1 defaults):
  WATCH_DERIV if either persists for 2 windows:
    - dRDR_dt >= +0.12
    - dFDR_dt <= -0.12
    - dBSI_dt >= +0.12
  TRUNCATE_DERIV if either occurs:
    - RDR spike > 1.4 (level spike)
    - dRDR_dt >= +0.20 AND O_r is low (Oracle collapse proxy)
    - dFDR_dt <= -0.20 AND P_repair is high (panic repair spiral)
STITCH CONFIRMATION (derivative):
  - dFDR_dt > 0 for 2 windows AND feasibility_strength rising
  - dRDR_dt < 0 for 2 windows
OUTPUTS (per window):
  FDR_s, RDR_s, BSI_s
  dFDR_dt, dRDR_dt, dBSI_dt
  DerivTrigger: NONE|WATCH|TRUNCATE|STITCH_CONFIRM

		

Add these columns to your CSV (HD upgrade)

Append:

TVIS_score
FDR_s,RDR_s,BSI_s
dFDR_dt,dRDR_dt,dBSI_dt
DerivTrigger

Operator-friendly interpretation (1 paragraph)

Derivatives turn TVIS into early warning: even if you’re still AMBER, a fast rise in dRDR/dt or fast drop in dFDR/dt indicates the system is accelerating toward irreversibility. Use derivative WATCH to act earlier (reduce load, restore Oracle metrics, demand feasibility markers) instead of waiting for sustained RED.

TVIS v0.1 — Case Packet Template (Standard Backtest Form)

Canonical ID: CivOS.Packet.TVIS.Case.v0.1
Purpose: A single reusable form to run any TVIS case (news/conflict/election/org/education) with consistent inputs, logs, and publishable outputs.
Status: LOCKED (forward-only)

A) Case Header (fill once)

			
[TVIS.CasePacket.v0.1]
CaseName:
CaseType: {Escalation|Election|Institution|Market|Education|Conversation|Other}
Lane: {GOV|DEF|FIN|EDU|...}
Place×Zoom: (optional) PlaceID × Z-level
TimeRange: start_date → end_date
RuptureEvent E:
RuptureTimestamp T0:
Horizon H (pre-window): ___ weeks/days
Cadence: {daily|weekly|monthly}
WindowSpec:
  N_target=800 tokens (min 300, max 1500)
  stride=50% overlap
Sources (2–4, triangulated):
  S1:
  S2:
  S3:
  S4 (optional):
Notes on selection (1–3 lines):

		

B) Data Collection Checklist (triangulation + anti-bias)

Required (pick at least 2)

[ ] Official statements / transcripts (primary)
[ ] Headline + lede samples from 2 outlets (different bias)
[ ] Domain documents (reports, memos, audits, earnings calls)

Optional (noisy)

[ ] Social media sampling (secondary only)

Anti-bias lock: if one source is sensational, it cannot be your only stream.

C) Window Build Instructions (repeatable)

			
Windowing:
  1) Merge texts in time order.
  2) Slice into windows Wt (N_target tokens; allow 300–1500).
  3) Keep cadence stable (e.g., weekly window per ISO week).
  4) For each window store:
       - timestamp t
       - source mix (S1/S2/S3 proportions)
       - excerpt (first 200–300 chars)

		

D) TVIS Run Sheet (per window log)

Paste this for each window (or generate from CSV):

			
t=____   window_id=____   sources={S1:__,S2:__,S3:__}
Core Intensities (per 1000 tokens):
  f=____  p=____  m+=____  m-=____
Ratios:
  FDR=____  RDR=____  BSI=____
Roles:
  V/O/R=____/____/____
  feasibility_strength(F3 share)=____
Score:
  TVIS_score=____  State=GREEN/AMBER/RED
Derivatives (HD):
  dFDR_dt=____  dRDR_dt=____  dBSI_dt=____
  DerivTrigger=NONE/WATCH/TRUNCATE/STITCH_CONFIRM
Diagnosis:
  FailureModeTag=____
  Fence=NONE/WATCH/TRUNCATE/STITCH
1-line note:
  _____________________________

		

E) Hit/Miss Scoring (falsifiable rubric)

Use the same rubric across cases.

			
Hit/Miss Rules:
  Strong Hit: sustained RED occurs ≥2 windows before T0 (k≥2)
  Weak Hit: AMBER→RED within 1 window of T0
  Miss: GREEN/AMBER until after T0
  False Alarm: sustained RED but no rupture inside horizon H
Record:
  First AMBER at t=____
  First RED at t=____
  Peak TVIS at t=____
  Lead time Δt = T0 - first_sustained_RED = ____ windows
Outcome classification: StrongHit/WeakHit/Miss/FalseAlarm

		

F) Required Failure Mode Trace (publishable, non-emotive)

			
Failure Mode Trace:
  1) __________________ (what changed in F/P/M)
  2) __________________ (which ratio crossed threshold)
  3) __________________ (role imbalance observed)
  4) __________________ (why this indicates irreversibility risk)
  Fence Counterfactual:
    - TRUNCATE action would have been: __________
    - STITCH path would have been: __________

		

G) FenceOS Counterfactual (choose the branch)

Tick the branch that matches the tag.

If Tag = FantasySpiral

[ ] Demand feasibility markers (budget/staff/timeline)
[ ] Convert V → staffed milestones (R)
[ ] Restore Oracle metrics (O) to prevent narrative inflation

If Tag = GrindTrap

[ ] Cut load (P↓), stop noncritical work
[ ] Add sequencing / scheduling (Operator routing)
[ ] Add small feasible wins (F3↑)

If Tag = CassandraTrap

[ ] Create O→R triggers (if-then actuation thresholds)
[ ] Assign owners and deadlines
[ ] Publish constraints + action plan

If Tag = RevisionistLoop / BlameCascade

[ ] Remove scapegoat/purity language from operator channels (M₋↓)
[ ] Reinstall Oracle layer (metrics + uncertainty)
[ ] Convert past into M₊ lessons + guardrails

If Tag = InstitutionalHollowing

[ ] Reduce compliance drag (P_compliance↓)
[ ] Restore pipelines (training/hiring) (F2/F3↑)
[ ] Convert reports into triggers (O→R bridge)

If Tag = PanicRepair

[ ] Stabilize: stop expansion
[ ] Rebuild feasibility and staffing
[ ] Postmortem → guardrails (M₊↑)

H) Publishable Results Summary (one page)

This is the section you paste into the final article.

			
Case Summary:
  CaseName: _______
  T0: _______
  Horizon H: _______
  Streams used: _______
TVIS Result:
  First AMBER: _______
  First RED: _______
  Peak TVIS: _______
  Lead time Δt: _______ windows
Primary Driver:
  {F collapse | P overload | M- takeover | RoleMismatch}
Primary Tag:
  _______
Secondary Tag:
  _______
Why models failed (1 paragraph):
  _____________________________
Fence counterfactual (1 paragraph):
  _____________________________
What to improve in v0.2 (optional, 2 bullets):
  - ___________________________
  - ___________________________

		

I) Minimal Attachments (recommended)

[ ] results.csv (window rows)
[ ] 3–6 key window excerpts (first AMBER, first RED, peak, T0)

TVIS v0.1 — Backtest Runner Prompt Pack (Copy/Paste)

Canonical ID: CivOS.PromptPack.TVIS.BacktestRunner.v0.1
Purpose: Prompts you can paste into any LLM/workflow to run a TVIS backtest end-to-end: ingest sources → window → lexicon count → ratios → state/tag/fence → CSV rows → publishable summary.
Status: LOCKED (forward-only)

How to use: Provide the LLM the text sources (paste or upload), plus the case header fields (T0, horizon, cadence). Then run prompts in order.

0) Master Instruction (paste once at the top)

			
You are running CivOS TVIS v0.1 backtests.
Rules:
- Use CivOS.Lexicon.TVIS.v0.1 to count weighted matches per window.
- Window spec: N_target=800 tokens (min 300, max 1500), stride=50% overlap.
- Normalize counts per 1000 tokens: f,p,m+,m-.
- Compute ratios:
  FDR=(f+0.7*m+0.25)/(p+0.25)
  RDR=(m-+0.25)/(f+0.25)
  BSI=(p+0.25)/(f+0.7*m+0.25)
- Compute RoleProfile V/O/R using the ROLE lexicon (sentence tagging by max score).
- Classify State:
  GREEN if FDR≥1.2 and RDR≤0.6
  AMBER if 0.9≤FDR<1.2 OR 0.6<RDR≤1.0 OR BSI≥1.2
  RED if FDR<0.9 OR RDR>1.0 OR RoleMismatch≥0.8
- Assign FailureModeTag using precedence:
  RevisionistLoop, BlameCascade, FantasySpiral, GrindTrap, CassandraTrap, PanicRepair, InstitutionalHollowing, Mixed
- Output results as CSV rows with the schema below.
- Be falsifiable: identify first AMBER, first sustained RED (k≥2), lead time to T0.
CSV columns:
idx,t,source_mix,N,f,p,m_plus,m_minus,FDR,RDR,BSI,V_r,O_r,R_r,feasibility_strength,RoleMismatch,TVIS_score,State,Tag,Fence,excerpt

		

1) Case Packet Prompt (fill-in form)

			
Create a TVIS Case Packet v0.1 header from the following:
CaseName:
CaseType:
Lane:
TimeRange:
RuptureEvent E:
RuptureTimestamp T0:
Horizon H:
Cadence:
Sources list (2–4):
Any notes:
Return the filled [TVIS.CasePacket.v0.1] header.

		

2) Source Normalization Prompt (clean the inputs)

			
I will provide multiple text sources with timestamps.
Normalize them into a single chronological list of entries.
For each entry:
- timestamp (as provided)
- source_id (S1/S2/S3/…)
- title (if present)
- body (cleaned; remove navigation, duplicates, ads)
- keep quotes as-is
Return:
1) NormalizedEntries[]
2) A short note on any missing timestamps or ambiguities.

		

3) Window Builder Prompt (token-based windows)

			
Using the normalized entries, build rolling windows:
Rules:
- Target 800 tokens per window (min 300, max 1500).
- Stride 50% overlap.
- Preserve approximate time ordering.
- If cadence is weekly: group into weeks first, then window within each week if needed.
For each window, return:
- idx
- window_time t (representative timestamp)
- source_mix (S1:%,S2:%,S3:%)
- token_count N
- window_text
- excerpt (first 280 chars)
Return WindowPack[].

		

4) Lexicon Count Prompt (F/P/M+/M− + feasibility_strength)

			
For each window in WindowPack[], compute lexicon counts using CivOS.Lexicon.TVIS.v0.1.
Requirements:
- phrase-first matching (phrases before single tokens)
- apply negation rule (within 4 tokens → weight * 0.4)
- apply intensifier rule (within 2 tokens → weight * 1.2)
- count weighted totals for:
  F1_commitment, F2_build, F3_feasibility, F_time_anchor,
  P_constraint, P_repair, P_cost_attrition, P_compliance_drag,
  Mplus_lessons, Mplus_guardrails,
  Mminus_nostalgia, Mminus_grievance, Mminus_purity_rollback, Mminus_scapegoat, Mminus_absolutism
Then compute:
- F_total = sum(F*)
- P_total = sum(P*)
- Mplus_total = sum(Mplus*)
- Mminus_total = sum(Mminus*)
Compute feasibility_strength:
feasibility_strength = F3_feasibility / max(1, (F1_commitment + F2_build + F3_feasibility))
Return WindowCounts[] keyed by idx.

		

5) Role Tagging Prompt (V/O/R proportions)

			
For each window_text:
- split into sentences (simple split on .?! is fine)
- score each sentence for V, O, R using ROLE lexicon terms
- assign sentence role = argmax(V,O,R), ties → Mixed
Return for each window:
- V_r, O_r, R_r (proportions)
- role_counts (V,O,R,Mixed)
Also note if Oracle language is "fake" (absolutism without metrics).
Return RoleProfile[] keyed by idx.

		

6) TVIS Compute Prompt (ratios + score + state)

			
For each window:
Given N, F_total, P_total, Mplus_total, Mminus_total:
Compute normalized intensities per 1000 tokens:
f = 1000*(F_total/N)
p = 1000*(P_total/N)
m_plus = 1000*(Mplus_total/N)
m_minus = 1000*(Mminus_total/N)
Compute ratios:
FDR=(f+0.7*m_plus+0.25)/(p+0.25)
RDR=(m_minus+0.25)/(f+0.25)
BSI=(p+0.25)/(f+0.7*m_plus+0.25)
Compute RoleMismatch flags:
- FantasySpiral if V_r>0.45 AND feasibility_strength low AND R_r<0.25
- GrindTrap if R_r>0.45 AND p high AND f low
- CassandraTrap if O_r>0.45 AND R_r<0.25 AND warning markers high
- Revisionist if m_minus high AND O_r<0.20
RoleMismatch = 0.5*FantasySpiral + 0.4*GrindTrap + 0.4*CassandraTrap + 0.6*Revisionist
Compute TVIS_score (0–100):
- rF = 1 - min(FDR,1.5)/1.5
- rR = min(RDR,2.0)/2.0
- rB = min(BSI,2.0)/2.0
- rM = min(RoleMismatch,1.2)/1.2
TVIS_score = 100 * clamp((rF+rR+rB+rM)/4, 0, 1)
State rules:
GREEN if FDR≥1.2 and RDR≤0.6
RED if FDR<0.9 OR RDR>1.0 OR RoleMismatch≥0.8
else AMBER
Return TVISRows[] keyed by idx.

		

7) Tag + Fence Prompt (FailureModeTag + WATCH/TRUNCATE/STITCH)

			
Assign FailureModeTag with precedence:
1) RevisionistLoop if Revisionist flag AND RDR>1.2
2) BlameCascade if scapegoat markers high AND RDR>1.0 AND O_r low
3) FantasySpiral if FantasySpiral flag
4) GrindTrap if GrindTrap flag
5) CassandraTrap if CassandraTrap flag
6) PanicRepair if P_repair high AND P_constraint high AND f low
7) InstitutionalHollowing if P_compliance_drag high AND P_cost_attrition high AND F3_feasibility low
Else Mixed
FenceTrigger logic (time series):
- WATCH if AMBER persists k=2
- TRUNCATE if RED persists k=3 OR RDR spike > 1.4
- STITCH if post-RED: FDR≥1.1 for 2 windows AND feasibility_strength rising
Return FinalRows[] with Tag and Fence.

		

8) Derivatives Prompt (HD early warning)

			
Compute smoothed ratios and slopes (EMA alpha=0.35):
FDR_s, RDR_s, BSI_s
dFDR_dt = FDR_s[i]-FDR_s[i-1]
dRDR_dt = RDR_s[i]-RDR_s[i-1]
dBSI_dt = BSI_s[i]-BSI_s[i-1]
DerivTrigger:
- WATCH if dRDR_dt≥+0.12 for 2 windows OR dFDR_dt≤-0.12 for 2 windows OR dBSI_dt≥+0.12 for 2 windows
- TRUNCATE if dRDR_dt≥+0.20 AND O_r low OR RDR>1.4 spike
- STITCH_CONFIRM if dFDR_dt>0 for 2 windows AND dRDR_dt<0 for 2 windows AND feasibility_strength rising
Append to rows and return.

		

9) CSV Emitter Prompt (final artifact)

			
Emit results as CSV text with header:
idx,t,source_mix,N,f,p,m_plus,m_minus,FDR,RDR,BSI,V_r,O_r,R_r,feasibility_strength,RoleMismatch,TVIS_score,State,Tag,Fence,DerivTrigger,excerpt
Ensure:
- numbers rounded to 3 decimals
- excerpt is max 180 chars, quotes escaped
Return the CSV only.

		

10) Publishable Summary Prompt (one-page report)

			
Write a publishable TVIS backtest summary using this structure:
1) Case Summary (CaseName, T0, H, streams used)
2) TVIS Result (first AMBER, first sustained RED k≥2, peak TVIS, lead time Δt)
3) Primary Driver (F collapse / P overload / M- takeover / RoleMismatch)
4) Primary Tag + Secondary Tag (if any)
5) Why models failed (1 paragraph)
6) Failure Mode Trace (4 lines, schematic)
7) Fence Counterfactual (1 paragraph)
8) Notes on false alarm / uncertainty (1 paragraph)
Keep it concise and falsifiable.

		

11) “Human-in-the-loop check” Prompt (quality control)

			
Before finalizing, list:
- Top 5 phrases that contributed most to M- (with counts)
- Top 5 feasibility markers found (F3)
- Any likely false positives (e.g., 'invasion' used metaphorically)
- Any missing data risks (e.g., no official statements available)
Then propose one minimal adjustment (if needed) without changing core TVIS rules.

		

Optional: Micro-Prompt for Live News Monitoring (non-backtest)

			
Given today's batch of text, compute one TVIS window and output:
State, Tag, Fence, and 3 bullets: why.
No backtest, no timeline.

TVIS Toolkit Page v0.1 (All-in-One Canonical Module)

Suggested slug: /civos-tvis-toolkit-v0-1/
Canonical ID: CivOS.Toolkit.TVIS.v0.1
Status: LOCKED (forward-only)
Purpose: A single executable-style page bundling TVIS: spec + lexicon + implementation stub + operator/oracle/visionary cards + case packet + runner prompts.

0) Quick Start (60 seconds)

If you only do one thing:

Take any text stream (news, speech, meeting notes).
Run TVIS windowing (≈800 tokens).
Count lexicon hits (F/P/M₊/M₋ + V/O/R).
Compute FDR/RDR/BSI and classify GREEN/AMBER/RED.
Apply Fence action: WATCH / TRUNCATE / STITCH.

1) Definition Lock

TVIS measures three time-forces in language:

F — Future Pull: credible next-state traction + feasibility markers
P — Present Load: constraints + repair + backlog + costs/attrition
M — Memory Bind:
M₊ Useful Memory (Wisdom): lessons + guardrails
M₋ Regressive Memory: nostalgia/grievance/scapegoat/purity rollback

Role overlay:

V (Visionary): direction/why
O (Oracle): sensing/metrics/constraints
R (Operator): execution/allocation/delivery

Lock: Wisdom = M₊ only.

2) TVIS Sensor Spec v0.1 (Canonical)

Canonical ID: CivOS.Sensor.TVIS.v0.1

Ratios (core)

Let f, p, m+, m− be intensities per 1000 tokens. Constants α=0.7, ε=0.25.

FDR = (f + α·m+ + ε) / (p + ε)
RDR = (m− + ε) / (f + ε)
BSI = (p + ε) / (f + α·m+ + ε)

State thresholds

GREEN: FDR ≥ 1.2 AND RDR ≤ 0.6
AMBER: 0.9 ≤ FDR < 1.2 OR 0.6 < RDR ≤ 1.0 OR BSI ≥ 1.2
RED: FDR < 0.9 OR RDR > 1.0 OR RoleMismatch ≥ 0.8

Fence triggers

WATCH: AMBER persists k=2 windows
TRUNCATE: RED persists k=3 OR RDR spike > 1.4
STITCH: post-RED recovery: FDR ≥ 1.1 for 2 windows + feasibility rising

3) TVIS Score (0–100)

Canonical ID: CivOS.Impl.TVIS.Score.v0.1

Compute normalized risks:

rF = 1 − min(FDR,1.5)/1.5
rR = min(RDR,2.0)/2.0
rB = min(BSI,2.0)/2.0
rM = min(RoleMismatch,1.2)/1.2

Then:

TVIS_score = 100 × clamp((rF+rR+rB+rM)/4, 0, 1)

4) Derivatives Add-on (HD)

Canonical ID: CivOS.Impl.TVIS.Derivatives.v0.1

Compute slopes with EMA smoothing (α=0.35):

dFDR/dt, dRDR/dt, dBSI/dt

Derivative triggers:

WATCH: dRDR/dt ≥ +0.12 (2 windows) or dFDR/dt ≤ −0.12 (2 windows) or dBSI/dt ≥ +0.12 (2 windows)
TRUNCATE: dRDR/dt ≥ +0.20 with low Oracle OR RDR spike > 1.4
STITCH_CONFIRM: dFDR/dt > 0 for 2 windows AND dRDR/dt < 0 for 2 windows AND feasibility rising

5) Lexicon File (Copy-Paste)

Canonical ID: CivOS.Lexicon.TVIS.v0.1
Use the lexicon pack you published (F/P/M+/M− plus V/O/R) and keep core stable.
(Paste your full lexicon block here — recommended to include lane-pack scaffold.)

6) Implementation Stub (No ML, CSV Output)

Canonical ID: CivOS.Impl.TVIS.Stub.v0.1

Pipeline:

tokenize → window → lexicon counts → roles → ratios → state/tag/fence → CSV
(Paste your full stub block here.)

Recommended CSV schema:

			
idx,t,source_mix,N,f,p,m_plus,m_minus,FDR,RDR,BSI,V_r,O_r,R_r,
feasibility_strength,RoleMismatch,TVIS_score,State,Tag,Fence,
dFDR_dt,dRDR_dt,dBSI_dt,DerivTrigger,excerpt

7) Operator / Oracle / Visionary Cards (Action Manuals)

7.1 Operator Card

Canonical ID: CivOS.Card.TVIS.Operator.v0.1

GREEN: maintain feasibility + metrics
AMBER: WATCH → reduce load + restore Oracle + add feasible milestones
RED: TRUNCATE → stop irreversible moves + remove scapegoat narratives + enforce metrics + convert vision into staffed deliverables
Recovery: STITCH → feasibility rising + FDR recovery + RDR drop

7.2 Oracle Card

Canonical ID: CivOS.Card.TVIS.Oracle.v0.1

add Verification Markers (VMark)
split F into commitment/build/feasibility
control false alarms via triangulation + baseline + negation handling
use derivatives for early warning

7.3 Visionary Card

Canonical ID: CivOS.Card.TVIS.Visionary.v0.1

Hope = feasible future pull
always publish V→O→R chain (direction → constraints → execution)
never use nostalgia as propulsion; convert past to M₊ guardrails

(Paste the one-page cards here or link to their pages.)

8) Case Packet Template (Run Any Backtest)

Canonical ID: CivOS.Packet.TVIS.Case.v0.1
Use the standard case header + window log + hit/miss rubric + failure trace + fence counterfactual.
(Paste your case packet template here.)

9) Backtest Runner Prompt Pack (Copy/Paste)

Canonical ID: CivOS.PromptPack.TVIS.BacktestRunner.v0.1
Prompts to ingest sources → build windows → compute counts → emit CSV → publish summary.
(Paste your prompt pack here.)

10) Backtest Series Index (Internal Links)

Hub ID: CivOS.Hub.TVIS.Backtests.v0.1

/civos-backtest-tvis-model-fail-v0-1/
/civos-backtest-tvis-escalation-drift-v0-1/
/civos-backtest-tvis-election-drift-v0-1/
/civos-backtest-tvis-institutional-hollowing-v0-1/
/civos-backtest-tvis-hype-spiral-v0-1/
/civos-backtest-tvis-repair-success-v0-1/

11) Failure Mode Trace (Canonical, required)

Every TVIS output should include a schematic trace:

			
F↓ + P↑ → FDR<1 → stall
M-↑ → RDR>1 → retrograde drift
O_r↓ + RoleMismatch → irreversibility risk
Fence: TRUNCATE → reduce P + restore O + inject feasible F + convert M- to M+
Stitch: feasibility rises + FDR>1.1 + RDR falls → GREEN recovery

		

12) Version Lock

Do not rename: CivOS.Toolkit.TVIS.v0.1
Forward-only: v0.2, v1.0
Keep the output schema stable so backtests remain comparable.

Start Here:

Start here if you want the full sequence:

Vocabulary OS Series Index:
https://edukatesg.com/vocabulary-os-series-index/

Fence English Learning System:

eduKateSG Learning Systems: