1) Diagnostic Identity

Diagnostic Name: Narrative–Metric Gap / Map

Short Name / Symbol: narrative_metric_gap

Diagnostic Class: Narrative Integrity / Metric Alignment / Proxy Drift / Sensemaking Verification

Primary Function: Estimate the divergence between the story a system tells about its condition, success, repair, legitimacy, or trajectory and the actual metric, outcome, recurrence, affected-node, and coherence evidence available.

Primary Use: Determine whether a system’s explanatory narrative accurately maps to observed effects, or whether narrative is being used to stabilize, defend, obscure, inflate, simplify, or replace reality contact.

Core Risk if Ignored: The system may preserve a success, repair, legitimacy, safety, or progress story while actual outcomes, hidden debt, recurrence, affected-node state, or coherence indicators diverge.

Core Risk if Overtrusted: Any narrative discrepancy may be treated as deception or incoherence, even when narrative is provisional, poetic, incomplete, early-stage, or attempting to describe real effects that metrics do not yet capture.

2) Mechanical Definition

narrative_metric_gap measures the distance between what the system says is happening and what the evidence shows is happening.

narrative_metric_gap answers:

Does the system’s story match the measurable and observable reality?

The “narrative” may include:

success story
repair story
legitimacy story
safety story
progress story
failure story
risk story
identity story
mission story
accountability story
coherence story

The “metric” side includes both formal and informal evidence:

quantitative metrics
observable outcomes
recurrence data
affected-node feedback
hidden debt indicators
stress-test results
repair durability
boundary condition
memory integrity
proxy-coherence comparison

This diagnostic does not reduce reality to metrics. It asks whether narrative and evidence remain in living contact.

A healthy system can tell stories, interpret meaning, and preserve symbolic depth, but its narrative must remain corrigible by evidence.

A dangerous pattern appears when:

narrative says repair
metrics show recurrence

narrative says success
affected nodes show depletion

narrative says safety
stress tests show fragility

narrative says coherence
hidden debt continues rising

3) What the Diagnostic Measures

Direct Measurement Target

narrative_metric_gap measures:

• gap between story and outcome
• gap between declared success and observed effect
• gap between repair claim and repair evidence
• gap between legitimacy claim and trust indicators
• gap between safety claim and stress behavior
• gap between progress story and hidden debt
• gap between coherence language and O indicators
• gap between compliance story and affected-node reality
• gap between public narrative and internal condition
• gap between stated values and actual operator sequence
• gap between official memory and recurrence
• gap between metric interpretation and metric meaning
• gap between symbolic claim and operational consequence
• gap between performance report and system health

Indirect / Proxy Signals

narrative_metric_gap can be estimated from:

• success claims alongside recurrence
• repair claims without affected-node validation
• safety claims contradicted by stress divergence
• improvement narrative while backlog rises
• legitimacy language while appeal trust declines
• coherence claims while hidden debt increases
• performance reports excluding boundary strain
• official memory omitting failed repair
• metrics improving while lived function worsens
• narrative becoming more polished as evidence weakens
• declining willingness to update the story
• contradiction between internal and external accounts
• repeated need to explain away exceptions
• symbolic acknowledgment replacing structural repair
• high Φ with falling O
• public confidence stronger than audit evidence

What It Does Not Measure

narrative_metric_gap does not directly measure:

• whether the narrative is intentionally false
• whether metrics are complete reality
• whether qualitative meaning is invalid
• whether symbolic interpretation is incoherent
• whether all narratives must be numerical
• whether metrics should dominate meaning
• whether early narrative must be fully proven
• whether a gap always means deception
• whether narrative should be eliminated
• whether metrics are unbiased
• whether affected-node reports are automatically complete

High narrative_metric_gap means narrative and evidence are diverging.

It does not automatically mean bad faith.

Low narrative_metric_gap means narrative and evidence are aligned enough for current use.

It does not mean the narrative is complete, final, or immune from future correction.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

• Φ: metrics, performance indicators, and success proxies often anchor narrative
• O: narrative must ultimately remain coherent with actual system coherence
• H: hidden debt often grows beneath success or repair narratives
• Au: auditability determines whether narrative can be compared to evidence
• ι: inversion risk rises when narrative preserves apparent coherence while fit degrades
• µᵢ: integrity depends on alignment between what is said, what is done, and what results

Secondary Variables

• ε: visible error may be minimized, overemphasized, or reinterpreted by narrative
• BΣ: boundary harm may be narratively softened, omitted, or reframed
• K: compatibility may be claimed narratively while coupling depletes one side
• R: restoration may be narrated as complete before repair actually lands

Variables Commonly Confused With narrative_metric_gap

5) Localization Signature

Primary Legibility Layers

• U4 — Classification / Metrics / Narratives: primary layer where story, interpretation, labels, metrics, and meaning are assembled
• U5 — Coordination / Time: where narrative may be premature, delayed, or used to close before recurrence windows pass
• U6 — Coherence Field: where narrative either preserves shared reality or fragments it
• U7 — Memory / Recurrence: where narrative becomes durable memory, precedent, canon, or institutional story
• U8 — Environment / Forcing: where exposure, crisis, public scrutiny, or external pressure tests narrative truth

Primary Leverage Layers

• U4: repair labels, story, metric interpretation, and claims
• U5: sequence narrative after evidence and recurrence validation
• U6: restore shared reality when narrative diverges
• U7: correct official memory and archive lineage
• U3: align action with claimed repair or success
• U2: repair constraints if narrative masks boundary or permission failure

Verification Layers

• U4: does the narrative match the evidence?
• U5: is the story sequenced after sufficient validation?
• U6: does narrative improve coherence or hide contradiction?
• U7: does memory preserve the correct story?
• U3: does behavior match the narrative?
• U8: does the narrative survive external forcing?

Common Mislocalizations

• Treating narrative polish as narrative truth
• Treating metrics as self-interpreting
• Treating story as deception by default
• Treating symbolic meaning as metric claim
• Treating compliance story as restoration
• Treating public narrative as system memory
• Treating internal dashboard as affected-node reality
• Treating narrative disagreement as data rejection
• Treating metric improvement as proof the story is true
• Treating affected-node contradiction as anecdotal noise
• Treating repair language as repair evidence
• Treating story consistency as coherence

6) Input Requirements

Required Inputs

To estimate narrative_metric_gap, the system needs:

• narrative claim being evaluated
• metrics or evidence used to support it
• affected variables in S
• stated success, repair, safety, legitimacy, or coherence claim
• observable outcomes
• affected-node feedback
• recurrence data
• hidden debt indicators
• metric lineage
• source provenance
• baseline and stressed evidence where relevant
• public versus internal narrative if applicable
• whether narrative changes after contradiction
• whether narrative has entered U7 memory

Optional Inputs

These improve precision:

• historical narrative versions
• official statements
• internal reports
• dashboard data
• audit records
• repair records
• stress-test results
• recurrence windows
• appeal data
• affected-node cost
• trust indicators
• backlog data
• exception data
• boundary-strain data
• source-to-summary mapping
• public/private communication comparison
• external review
• downstream memory use

Missing Input Behavior

If narrative_metric_gap inputs are missing:

• If metrics are missing, treat narrative as interpretive but unverified
• If narrative is missing, inspect implicit story in action and classification
• If affected-node feedback is missing, do not declare narrative complete
• If recurrence data is missing, avoid repair-complete narrative
• If metric lineage is missing, check Φ−O risk
• If Au_eff is low, do not overtrust either narrative or metric
• If FI_integrity is weak, narrative may be protected from contradiction
• If U7 memory is already bound, check memory contamination risk

Default missing-input posture:

treat narrative as provisional → map evidence → compare outcomes → include affected-node signal → update story only after validation

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

Narrative accurately maps to evidence, preserves uncertainty, and updates when reality changes.

Signals:

• narrative matches observed outcomes
• metrics are interpreted with scope
• affected-node feedback is included
• recurrence data supports repair claims
• hidden debt indicators are acknowledged
• stress-test evidence is represented accurately
• uncertainty is preserved
• narrative revises with new evidence
• public and internal accounts are compatible
• U7 memory stores claim with provenance

Recommended posture:

use narrative for Μ / Τ / communication
maintain Au/FI linkage
validate over recurrence
preserve metric scope

Watch Range

Narrative is mostly aligned but showing signs of overreach, omission, premature closure, or selective evidence.

Signals:

• success language is stronger than evidence
• repair claim is plausible but recurrence window is incomplete
• affected-node feedback is partial
• metrics are narrow
• hidden debt is uncertain
• narrative omits caveats
• public story is cleaner than internal record
• stress evidence is incomplete
• symbolic language risks being read as operational proof

Recommended posture:

add scope and uncertainty
include missing evidence
delay closure claims
check Φ−O and AckDebt
preserve provisional wording

Degraded Range

Narrative diverges from evidence enough to distort action, repair, memory, or legitimacy.

Signals:

• repair story persists despite recurrence
• success story persists despite affected-node strain
• safety story fails under stress
• legitimacy story conflicts with appeal/trust indicators
• metrics are cherry-picked
• hidden debt is omitted
• public narrative contradicts internal evidence
• official memory stores incomplete story
• feedback challenging narrative is ignored
• narrative defends Φ while O degrades

Recommended posture:

pause narrative closure
activate Ξ
restore Au/FI
compare narrative to O/H/recurrence
revise official memory
repair omitted debt

Contraindicated:

public certainty
repair-complete claims
scaling from narrative
canonizing success story
punitive response to contradiction
deep coupling based on narrative trust

Critical / Collapse-Prone Range

Narrative becomes an inversion engine that protects pseudo-coherence from correction.

Signals:

• narrative is immune to contrary evidence
• metrics are reinterpreted to preserve story
• affected-node reality is excluded
• recurrence is renamed to avoid contradiction
• official memory preserves false repair or success
• legitimacy depends on maintaining narrative
• exposure would cause narrative collapse
• feedback cannot update the story
• narrative replaces audit
• hidden debt becomes structurally protected

Recommended posture:

stop narrative-dependent actuation
preserve evidence
activate Ξ / Au / FI review
reopen official memory
include affected-node signal
repair hidden debt
revalidate before any success or legitimacy claim

False Positive Risk

narrative_metric_gap may appear high when:

• metrics are incomplete but narrative is capturing real qualitative change
• repair is happening before metrics update
• affected-node recovery is delayed but improving
• narrative is aspirational and clearly marked as such
• symbolic language is being mistaken for metric claim
• early-stage coherence is visible before formal measurement
• metrics are poorly designed
• story preserves meaning that metrics cannot capture

False Negative Risk

narrative_metric_gap may appear low when:

• metrics are selected to fit narrative
• affected-node signal is suppressed
• hidden debt is unmeasured
• recurrence window is too short
• public story and dashboard are aligned but both miss O
• narrative is polished enough to appear coherent
• official memory excludes contradictions
• low EB hides counter-signal
• stress tests are absent or too weak

8) Leading Indicators

narrative_metric_gap degradation appears early as:

• narrative becomes more polished while evidence becomes thinner
• caveats disappear
• metrics are repeated without interpretation
• affected-node contradiction is minimized
• repair claims appear before recurrence validation
• success language outruns outcome data
• public story diverges from internal uncertainty
• stress-test failures are omitted
• old failures are renamed
• hidden debt is described as temporary inconvenience
• symbolic acknowledgment replaces repair evidence
• criticism is treated as misunderstanding
• the story becomes harder to revise
• metrics that challenge narrative are deprioritized
• narrative coherence becomes more important than evidence coherence

9) Lagging Indicators

narrative_metric_gap failure has already accumulated debt when:

• legitimacy shock follows exposure
• official narrative must be retracted
• recurrence disproves repair claims
• affected nodes reject the story
• external audit contradicts internal account
• hidden debt surfaces all at once
• public trust collapses
• memory correction becomes necessary
• the success story becomes evidence of failure
• system cannot name the divergence without destabilization
• prior metrics are abandoned or redefined
• repair must start by correcting the story

10) Interpretation Rules

How to Read narrative_metric_gap

narrative_metric_gap should be read as:

alignment between system story and evidence field

It is not a rejection of narrative.

A system may have:

• strong narrative and strong evidence — healthy communication
• strong narrative and weak evidence — overclaim risk
• weak narrative and strong evidence — under-communicated truth
• conflicting narratives and partial evidence — sensemaking phase
• metrics and narrative aligned but both disconnected from O
• narrative capturing qualitative change before metrics catch up
• narrative preserving old memory after evidence changes

What Changes Its Meaning

narrative_metric_gap changes meaning under:

• high Φ−O
• low Au_eff
• weak FI_integrity
• low EB
• low M_int(t)
• high AckDebt
• high AP(t)
• high Cv(t)
• high stress_divergence
• high recovery_asymmetry
• high X_c(t)
• low affected-node access
• public legitimacy pressure
• durable U7 memory binding
• high symbolic charge

Context Modifiers

High Φ−O: metrics may support narrative while coherence falls.

Low Au_eff: story cannot be checked against source.

Weak FI: narrative cannot be falsified.

Low EB: contradicting signal may not appear.

Low M_int(t): official memory may preserve distorted narrative.

High AckDebt: narrative may omit necessary recognition.

High AP(t): narrative may collapse into blame or exoneration.

High recovery_asymmetry: narrative may overstate repair.

High stress_divergence: narrative may rely on baseline evidence only.

Domain Calibration Notes

narrative_metric_gap should be calibrated by domain:

• in engineering: incident narrative versus logs, recurrence, reliability, and affected user impact
• in AI: model capability/safety narrative versus evals, edge cases, memory/tool behavior, and user outcomes
• in institutions: reform narrative versus recurrence, trust, affected-node cost, and service outcomes
• in governance: public legitimacy narrative versus service delivery, rights protection, appeals, and remedy data
• in relationships: repair story versus changed behavior, recurrence, boundary trust, and shared memory
• in archives: canon narrative versus glossary consistency, source lineage, cross-link health, and reader comprehension

11) Operator Sequencing Implications

If narrative_metric_gap Is Low / Healthy

Allowed with ordinary gate checks:

• Μ can use narrative for shared sensemaking
• Τ can use narrative for trajectory orientation
• Γ can select from narrative-supported evidence
• Π can constrain using accurate story/metric relationship
• ℛ can use narrative to coordinate repair
• U7 can store narrative as memory with provenance
• communication can proceed with scope and caveats

Recommended:

evidence → Μ narrative synthesis → Au/FI check → U7 memory with scope → recurrence validation

If narrative_metric_gap Is High

Recommended:

pause closure → compare narrative to metrics/outcomes/O/H → activate Ξ → revise story → repair omitted debt → update U7

Or:

separate story / metric / affected-node reality / hidden debt → rebuild narrative from source

Avoid or delay:

• public certainty
• repair-complete claims
• legitimacy claims
• scaling from narrative
• canonizing narrative
• punitive response to contradiction
• deep coupling based on narrative trust
• using narrative as evidence

Operators Recommended Under High Gap

• Ξ: detect narrative pseudo-coherence
• Au: reconstruct source and evidence
• FI: allow contradiction to update story
• Μ: rebuild sensemaking
• Θ: damp confidence in story
• ℛ: repair hidden debt omitted by narrative
• Γ: select revised claims and evidence weights
• Π: constrain narrative use until verified

Operators Contraindicated Under High Gap

• Γ hard selection: may select from story rather than reality
• Π irreversible constraint: may encode false narrative
• ⊗ deep coupling: may bind trust around inaccurate story
• ⊕ composition: embeds narrative distortion into identity
• Τ acceleration: scales story before evidence
• Σ escalation: sacralizes unverified narrative
• ✕ force: enforces story over reality

12) Gate Implications

Gates Strengthened By Reliable narrative_metric_gap Reading

• Au-Actuation: narrative can be traced to evidence
• FI-Gate: feedback can falsify the story
• High Risk Gate: blocks high-risk binding from narrative overreach
• MS-Gate: checks whether narratives differ by rank or node
• ☷ᵢ: prevents principle language from becoming narrative shield

Gates Weakened If narrative_metric_gap Is Poorly Known

If narrative/metric alignment is unknown:

• Au may trace only the story, not evidence
• FI may not challenge official account
• High Risk Gate may bind success, repair, or attribution claims too early
• MS may miss rank-protective narratives
• ☷ᵢ may enforce slogan rather than principle
• Π may constrain based on narrative
• Γ may select from story-framed options
• ℛ may repair narrative image rather than origin-layer debt

Gate Outcomes Affected

High narrative_metric_gap should push gates toward:

• Pause story-based closure
• Require evidence comparison
• Require affected-node validation
• Require recurrence data
• Require metric lineage
• Deny repair-complete narrative
• Deny legitimacy claims
• Deny durable U7 story binding
• ∅ for high-impact action justified primarily by narrative rather than evidence

13) Scaling Behavior

narrative_metric_gap becomes more dangerous under scale because narratives travel faster, wider, and more emotionally than evidence.

As systems scale:

• stories become slogans
• metrics become symbolic proof
• source caveats disappear
• public narratives outrun internal evidence
• official memory hardens
• contradictions are costly to acknowledge
• legitimacy depends on story stability
• affected-node signal is compressed
• dashboards reinforce narrative
• repair narratives become templates
• narrative repetition increases perceived truth
• metrics are selected to support story
• stress failures are reframed
• downstream actors inherit story without source

Scaling Risks

• legitimacy shock
• narrative capture
• official-memory distortion
• repair theater
• success theater
• metric theater
• public/private divergence
• source erasure
• sloganization
• Goodhart risk
• hidden debt protection
• affected-node invisibility
• canon drift
• false readiness
• trust collapse after exposure

Scaling Requirements

To scale narratives safely, systems need:

• source lineage
• metric lineage
• caveat preservation
• public/internal alignment
• affected-node signal inclusion
• recurrence validation
• stress-test disclosure
• narrative revision pathway
• memory correction pathway
• narrative confidence labels
• distinction between aspiration and evidence
• Φ/O checks
• hidden debt indicators
• independent audit triggers
• contradiction handling
• story-to-action verification

Scaling Rule

Narrative authority must scale only with evidence integrity, feedback integrity, recurrence validation, and memory accuracy.

Sanity constraint:

narrative_authority > evidence_support ⇒ narrative debt ↑

If a story gains more authority than evidence supports, narrative debt accumulates.

Second constraint:

narrative_metric_gap ↑ + U7 binding ↑ ⇒ official-memory distortion risk ↑

If a divergent story becomes durable memory, correction becomes harder.

Third constraint:

narrative_metric_gap ↑ + legitimacy dependence ↑ ⇒ legitimacy shock risk ↑

If legitimacy depends on a story that evidence cannot support, exposure becomes destabilizing.

14) Interaction / Coupling Behavior

narrative_metric_gap reveals whether a relation, institution, archive, AI system, or interface is coordinating from a shared story that matches reality.

What It Reveals About Coupling

• whether one node’s story matches another node’s experience
• whether repair narrative matches repair reality
• whether compatibility is narrated rather than demonstrated
• whether trust rests on evidence or story
• whether one node’s success story depends on another’s hidden cost
• whether narrative alignment hides boundary strain
• whether shared memory is accurate
• whether re-coupling is based on verified repair or hopeful story

What It Reveals About Boundary Integrity

Boundary harm is often hidden by narrative.

When narrative_metric_gap is high:

• boundary strain may be reframed as misunderstanding
• refusal may be narrated as resistance
• consent may be inferred from silence
• repair may be narrated as complete
• access may be framed as trust
• BΣ degradation may be hidden beneath unity stories
• affected-node signal may contradict official account

What It Reveals About Compatibility

Compatibility requires that the shared story be correctable by reality.

A coupling may be unsafe if:

the relationship works only while one story dominates the evidence field

or:

one node’s narrative of repair is another node’s memory of unresolved harm

Healthy compatibility allows narrative revision without collapse.

Relevant Interface Acts

• ↺ Reflection: compare story to observed effect
• ⇩ Relaxation: reduce defensive pressure around narrative
• ⊘ Attenuation: reduce coupling when story and reality diverge
• ⊙ Alignment: align one’s own narrative with evidence
• →? Invitation: invite evidence and correction
• ⚕︎ Restorative Override: requires post-action narrative/evidence review
• ✕ Force: dangerous when used to impose narrative over reality

15) Failure Modes Detected

Primary Failure Modes

narrative_metric_gap detects or predicts:

• success theater
• repair theater
• compliance theater
• legitimacy theater
• official-memory distortion
• metric cherry-picking
• public/private divergence
• hidden debt protection
• affected-node invisibility
• proxy defense
• narrative capture
• sloganization
• false readiness
• canon drift
• source erasure
• recurrence denial
• symbolic acknowledgment without repair
• story-driven action

Composite Regimes Where narrative_metric_gap Matters

• Goodhart Collapse: narrative defends Φ while O falls
• Pseudo-Coherent Basin: story stabilizes hidden debt
• Repair Theater: repair narrative replaces repair evidence
• Mission Lock: story preserves trajectory against contradiction
• Taboo Lock: narrative becomes protected from audit
• Crisis Loop: failure recurs while story claims resolution
• Extraction Regime: success story hides exported cost
• Coercive Fusion: unity narrative hides boundary erosion
• LOS: formal story diverges from latent operation

16) Accountability & Reintegration Implications

If narrative_metric_gap Was Ignored

Likely consequences:

• story outran evidence
• repair was declared too early
• affected-node reality was omitted
• hidden debt accumulated
• public and internal reality diverged
• official memory stored false success
• recurrence contradicted the narrative
• legitimacy shock followed exposure
• metrics were used to shield story
• repair had to begin with narrative correction

Accountability questions:

• What story was told?
• What metrics supported it?
• What evidence contradicted it?
• What was omitted?
• Did affected nodes validate it?
• Did recurrence support it?
• Did stress testing support it?
• Did hidden debt decline?
• Did public language match internal evidence?
• Did the story become memory?
• Who benefited from the narrative?
• Who carried the cost of the gap?

If narrative_metric_gap Was Misread

Possible misread forms:

• symbolic meaning mistaken for metric claim
• early truthful narrative dismissed because metrics lag
• qualitative evidence dismissed as narrative
• aspiration mistaken for deception
• narrative revision mistaken for inconsistency
• metrics treated as superior by default
• story complexity mistaken for evasion
• affected-node narrative treated as subjective only
• provisional story treated as official claim
• poetic/symbolic compression treated as operational assertion

Required Restoration

When narrative_metric_gap failure is found:

identify narrative claim
→ map supporting and contradicting evidence
→ include affected-node signal
→ compare Φ / O / H / recurrence
→ revise story
→ correct U7 memory
→ repair hidden debt omitted by narrative
→ validate through future evidence

If narrative gap protected some nodes or burdened others, MS-Gate should review credit, blame, repair, and recognition distribution.

17) Cross-Domain Examples

Technical / Engineering

A postmortem says the incident is resolved because the immediate bug is patched, but recurring failures show the architecture remains fragile.

Diagnostic implication: repair narrative exceeds repair evidence.

Operator sequence: recurrence audit → root-cause repair → update postmortem → U7 incident memory correction.

Institutional / Governance

An institution announces reform, but affected-node reports and recurrence data show the same pattern continues.

Diagnostic implication: legitimacy narrative diverges from restoration evidence.

Operator sequence: affected-node validation → metric/recurrence audit → narrative correction → origin-layer repair.

AI / Algorithmic

A model is described as safer because benchmark scores improved, but real user edge cases show persistent failures.

Diagnostic implication: safety narrative is overfitted to metric evidence.

Operator sequence: Φ/O check → edge-case eval → feedback integration → model/tool/policy repair → narrative update.

Interaction / Relational

A relationship story says “we repaired this,” but the same boundary strain recurs and one person does not experience the repair as landed.

Diagnostic implication: repair narrative diverges from recurrence and affected-node state.

Operator sequence: ↺ reflection → name recurrence → boundary repair → U7 memory correction → Λ re-test.

Archive / Framework Design

A module is described as canon-ready, but glossary links, status labels, and cross-module dependencies are still inconsistent.

Diagnostic implication: archive readiness narrative exceeds archive integration metrics.

Operator sequence: cross-link audit → glossary repair → status correction → reader stress-test → canon update.

18) Test Protocols

1. Narrative Claim Test

What exactly is the story claiming?

Failure signal: narrative is vague enough to avoid verification.

2. Evidence Map Test

What evidence supports and contradicts the narrative?

Failure signal: only supporting evidence is included.

3. Metric Lineage Test

Do the metrics mean what the narrative says they mean?

Failure signal: metric scope is overextended.

4. Affected-Node Test

Do affected nodes validate the narrative?

Failure signal: official story and impacted reality diverge.

5. Recurrence Test

Does recurrence support or contradict the story?

Failure signal: same issue returns after resolution narrative.

6. Stress Test

Does the narrative hold under stress or only baseline?

Failure signal: success story fails under pressure.

7. Hidden Debt Test

Does H decrease under the narrative?

Failure signal: story improves while debt rises.

8. Public/Internal Alignment Test

Does public narrative match internal uncertainty?

Failure signal: external story is stronger than internal evidence.

9. Memory Binding Test

Has the narrative entered U7 as fact?

Failure signal: story becomes memory before validation.

10. Revision Test

Can the narrative update after contradiction?

Failure signal: story is protected from evidence.

19) Anti-Patterns

• Story as evidence
• Metric as story proof
• Compliance as repair
• Public statement as restoration
• Success story before recurrence window
• Repair story without affected-node validation
• Dashboard as reality
• Symbolic acknowledgment as structural correction
• Narrative polish as coherence
• Official memory as truth
• Internal uncertainty hidden by public certainty
• Affected-node contradiction as anecdote
• Stress failure omitted from success story
• Hidden debt called transition cost
• Repetition as validation
• Aspiration presented as evidence
• Story protected from feedback
• Narrative correction treated as betrayal
• Metrics chosen to fit story
• Canon story before integration

20) Spec Validation Check

• Is this truly a diagnostic, not an operator? Yes.
• Does it measure state, capacity, risk, or response rather than act directly? Yes.
• Does it map to S? Yes.
• Are U-layers specified? Yes.
• Are leading and lagging indicators separated? Yes.
• Are interpretation risks defined? Yes.
• Are operator sequencing implications clear? Yes.
• Are gate implications clear? Yes.
• Are scaling risks included? Yes.
• Are interaction implications included? Yes.
• Does it avoid new primitives? Yes.

Condensed Archive Summary

narrative_metric_gap Narrative–Metric Gap / Map is the diagnostic estimate of the divergence between the story a system tells about its success, repair, safety, legitimacy, coherence, failure, or trajectory and the actual evidence field of metrics, outcomes, recurrence, affected-node signal, hidden debt, stress behavior, and coherence indicators. It does not reduce reality to metrics or reject symbolic narrative; it tests whether narrative remains corrigible by evidence. High narrative_metric_gap indicates risk of success theater, repair theater, official-memory distortion, metric cherry-picking, hidden debt protection, affected-node invisibility, legitimacy shock, proxy defense, and story-driven action. Under high gap, the system should pause narrative closure, compare story to Φ/O/H/recurrence, restore Au/FI, include affected-node signal, revise official memory, and repair omitted debt before public certainty, scaling, canonization, repair-complete claims, or narrative-dependent actuation.