Universal Theories

1) Diagnostic Identity

Diagnostic Name: Feedback Integrity

Short Name / Symbol: FI_integrity

Diagnostic Class: Feedback / Falsification / Correction / Anti-Goodhart / Reality Contact

Primary Function: Estimate whether feedback can accurately enter the system, remain connected to reality, challenge preferred outcomes, alter behavior, update memory, and trigger correction.

Primary Use: Determine whether the system can be corrected by reality rather than only receiving feedback that confirms, protects, or optimizes its existing trajectory.

Core Risk if Ignored: The system may collect feedback while remaining unfalsifiable, producing Goodhart effects, pseudo-learning, repair theater, memory contamination, proxy divergence, and hidden debt accumulation.

Core Risk if Overtrusted: Feedback channels are mistaken for feedback integrity; the system assumes that because feedback exists, correction is occurring.

2) Mechanical Definition

FI_integrity measures whether feedback can actually falsify, correct, redirect, or update the system rather than being filtered, absorbed, ignored, punished, gamed, or converted into confirmation.

FI_integrity answers:

Can feedback change the system when the system is wrong?

Feedback Integrity is not the same as feedback volume.

A system may receive many reports, surveys, comments, metrics, complaints, evaluations, or signals while still having low FI_integrity if the feedback:

does not reach decision nodes
cannot challenge preferred outcomes
is filtered before interpretation
is converted into performance metrics
is collected but not acted on
is punished when inconvenient
cannot update memory
cannot change constraints
cannot alter trajectory

FI_integrity is one of the main anti-Goodhart diagnostics because systems often preserve the appearance of responsiveness while feedback loses the power to correct.

3) What the Diagnostic Measures

Direct Measurement Target

FI_integrity measures:

feedback validity
feedback independence
feedback-to-action linkage
feedback-to-memory linkage
feedback-to-repair linkage
ability of feedback to falsify preferred outcomes
ability of feedback to challenge Φ
ability of feedback to update O models
ability of feedback to revise classifications
ability of feedback to modify constraints
ability of feedback to reach authority
ability of affected-node feedback to enter correction pathways
whether feedback changes future operator sequencing
whether contradiction can survive interpretation
whether feedback can reduce hidden debt

Indirect / Proxy Signals

FI_integrity can be estimated from:

feedback_action_ratio
repeated feedback with no behavior change
feedback channels that do not affect decisions
affected-node signal being filtered or summarized away
feedback being collected after decisions are already closed
feedback that can only adjust surface features
metrics improving while reported strain persists
complaints or reports recurring without correction
dissent being reframed as misunderstanding
feedback being punished, ignored, or reputationally costly
official feedback loops producing only confirmation
repair claims not changing after contradiction
classification remaining stable despite new evidence
U7 memory not updating after feedback
dashboard metrics replacing direct feedback

What It Does Not Measure

FI_integrity does not directly measure:

whether all feedback is true
whether feedback is high quality
whether every request should be obeyed
whether dissent is always coherent
whether feedback volume is high
whether feedback is emotionally easy to receive
whether feedback should override constraints
whether repair is complete
whether feedback has enough localization
whether action should occur immediately
whether the system has enough resources to respond

High FI_integrity means feedback can meaningfully correct the system.

It does not mean all feedback should be accepted as accurate.

Low FI_integrity means feedback is blocked, distorted, absorbed, or non-consequential.

It does not mean feedback is absent.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

O: feedback must help the system recover or preserve coherence
H: hidden debt accumulates when feedback cannot reveal or reduce it
ε: feedback often reports visible error, disturbance, or deviation
Au: feedback requires traceability to become actionable and verifiable
R: restoration depends on feedback reaching repair pathways
Φ: feedback integrity is required to prevent proxy success from replacing coherence

Secondary Variables

ι: inversion risk rises when feedback loops confirm pseudo-coherence
µᵢ: agent integrity depends on updating action after feedback
BΣ: boundary integrity requires feedback about strain, violation, refusal, or crossing
K: compatibility depends on whether feedback can revise coupling behavior

Variables Commonly Confused With FI_integrity

Variable / Diagnostic	Difference from FI_integrity
EB	Whether signal can be expressed; FI_integrity asks whether feedback can correct the system
signal_quality	Cleanliness of signal; FI_integrity asks whether feedback can affect outcomes
Au_eff	Traceability; FI_integrity requires traceability but also correction power
feedback_action_ratio	A useful sub-measure of whether feedback changes behavior
review_capacity	Capacity to evaluate feedback; FI_integrity asks whether evaluation can lead to correction
R_eff	Repair capacity; FI_integrity asks whether feedback can reach and shape repair
Φ − O	Proxy divergence; low FI_integrity often allows Φ−O to grow
Transparency	Visibility of information; FI_integrity requires falsification and correction, not just visibility

5) Localization Signature

Primary Legibility Layers

U3 — Execution: where feedback should alter behavior, process, output, or repair
U4 — Classification / Metrics / Narratives: where feedback is interpreted, categorized, accepted, rejected, or distorted
U5 — Coordination / Time: where feedback is routed, sequenced, escalated, delayed, or closed
U6 — Coherence Field: where feedback either restores shared reality or is absorbed into pseudo-coherence
U7 — Memory / Recurrence: where feedback becomes learning, precedent, correction, or is forgotten

Primary Leverage Layers

U2: design feedback gates, protections, permissions, and correction rights
U3: connect feedback to execution changes
U4: improve interpretation categories and anti-confirmation filters
U5: route feedback to authority and repair windows
U7: store feedback with source, action, and outcome linkage

Verification Layers

U3: did behavior change after feedback?
U4: was feedback interpreted without distortion?
U5: did feedback reach the right decision or repair node in time?
U6: did feedback improve coherence?
U7: did memory update and recurrence decline?

Common Mislocalizations

Treating feedback collection as feedback integrity
Treating surveys as correction
Treating complaint volume as feedback quality
Treating response statements as behavior change
Treating metrics as direct feedback
Treating public feedback as affected-node feedback
Treating acknowledgment as feedback integration
Treating low complaint rate as low problem rate
Treating dissent as noise
Treating feedback after closure as meaningful review
Treating feedback routing as repair
Treating dashboard changes as coherence changes

6) Input Requirements

Required Inputs

To estimate FI_integrity, the system needs:

feedback channel being evaluated
feedback source
feedback type
feedback destination
feedback quality
feedback localization
affected variables in S
decision or repair pathway
whether feedback reaches authority
whether feedback can falsify preferred outcomes
whether feedback can change behavior
whether feedback can update U7 memory
whether feedback is protected from suppression
whether affected nodes can provide feedback
whether feedback outcome is traceable

Optional Inputs

These improve precision:

feedback_action_ratio
feedback backlog
feedback closure records
response time
correction history
recurrence after feedback
affected-node validation
dissent suppression indicators
retaliation or penalty risk
source-to-summary trail
review capacity
escalation path
feedback triage categories
false positive / false negative history
rejected feedback records
public/private feedback divergence
metric-feedback comparison
independent audit of feedback outcomes

Missing Input Behavior

If FI_integrity inputs are missing:

If feedback destination is unknown, do not assume feedback can correct
If action linkage is unknown, treat feedback integrity as unverified
If affected-node access is unknown, assume feedback field may be incomplete
If feedback quality is unknown, avoid acting strongly without signal review
If feedback localization is unknown, avoid repair closure
If memory update is unknown, recurrence risk remains
If retaliation/cost is unknown, EB may be lower than assumed
If Φ pressure is high, check whether feedback threatening metrics is filtered
If Au_eff is low, feedback outcomes may be unverifiable

Default missing-input posture:

preserve feedback → trace route → verify interpretation → link to action → update memory → test recurrence

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

Feedback can enter, remain intelligible, reach the correct node, challenge preferred outcomes, change behavior, and update memory.

Signals:

affected nodes can provide feedback
feedback can contradict official narratives
feedback reaches decision/repair authority
feedback is traceable from source to action
feedback can revise classifications
feedback can change constraints or behavior
feedback outcomes are visible
recurrence declines after feedback integration
feedback is preserved in U7 memory
feedback threatening Φ is not automatically filtered

Recommended posture:

use feedback for Μ / Γ / Π / ℛ
maintain Au linkage
validate recurrence
preserve affected-node signal

Watch Range

Feedback exists and sometimes changes the system, but integrity is partial, slow, selective, or uneven.

Signals:

feedback is collected but action linkage is inconsistent
some feedback reaches authority, some is filtered
feedback requires repeated escalation
affected-node feedback is summarized too heavily
feedback updates minor issues but not root causes
feedback can change U3 but not U2/U4 structures
feedback is preserved but not tied to outcomes
recurrence declines slowly or unevenly

Recommended posture:

increase feedback-to-action linkage
restore source context
improve routing
protect affected-node signal
track recurrence after feedback

Degraded Range

Feedback is present but cannot meaningfully correct the system.

Signals:

feedback repeats without behavior change
feedback channels are performative
inconvenient feedback is reframed or ignored
affected-node feedback does not reach authority
metrics override lived signal
classification remains stable after contradiction
repair claims do not change after feedback
feedback disappears into summaries
feedback closure occurs without correction
recurrence persists despite repeated reports

Recommended posture:

pause closure claims
restore FI pathway
increase Au source-to-action trace
protect EB
repair decision linkage

Contraindicated:

declaring responsiveness
scaling from feedback metrics
repair-complete claims
punitive response to repeated feedback
durable U7 closure memory

Critical / Collapse-Prone Range

Feedback loops are captured, suppressed, inverted, or converted into system protection.

Signals:

feedback cannot falsify official claims
dissent is punished or excluded
all feedback becomes confirmation
affected-node reality is overwritten
metrics replace feedback entirely
feedback is used to optimize appearance
recurrence is renamed rather than repaired
system cannot hear contradiction without destabilizing itself
U7 memory stores closure despite unresolved feedback
legitimacy shock is likely when ignored feedback surfaces

Recommended posture:

stop feedback-dependent legitimacy claims
preserve suppressed signal
activate Ξ / Au / FI repair
restore affected-node access
rebuild feedback-to-action pathway
correct U7 memory
validate with recurrence

False Positive Risk

FI_integrity may appear high when:

feedback channels are numerous but non-consequential
response messages are fast but behavior is unchanged
feedback metrics improve while affected-node reality does not
feedback is collected after decisions are already closed
low complaint volume reflects low EB or high cost
feedback is routed but not empowered
feedback changes surface behavior but not cause
public feedback is visible while affected-node feedback is absent

False Negative Risk

FI_integrity may appear low when:

feedback is being processed carefully before action
high-quality feedback is low-volume
correction is happening at deeper layers with delayed visibility
the system is rejecting low-quality feedback appropriately
feedback appears unresolved because recurrence window has not passed
disagreement remains because feedback is plural and requires sorting
feedback integration temporarily increases visible ε or H exposure

8) Leading Indicators

FI_integrity degradation appears early as:

feedback repeats without visible change
feedback is acknowledged but not routed
feedback channels become formulaic
feedback summaries lose source detail
affected nodes stop reporting
dissent moves to private channels
feedback that lowers Φ is deprioritized
closure language appears before correction
feedback outcomes are not traceable
feedback becomes a metric rather than a correction path
review meetings increase while changes do not
contradictions are categorized as misunderstanding
feedback modifies wording but not structure
recurrence appears after “feedback addressed”

9) Lagging Indicators

FI_integrity failure has already accumulated debt when:

ignored feedback surfaces as crisis
legitimacy shock follows exposure
affected nodes exit or disengage
recurrence persists after repeated reports
repair claims are no longer believed
feedback records show known issues were uncorrected
external audit validates suppressed feedback
feedback channels lose trust
system memory stores false closure
hidden debt becomes active failure
public narrative reverses after long feedback suppression
real correction requires rebuilding the feedback system itself

10) Interpretation Rules

How to Read FI_integrity

FI_integrity should be read as:

context-specific ability of feedback to correct the system

It is not feedback volume, satisfaction score, or channel count.

A system may have:

high EB but low FI — expression appears but does not change anything
high Au but low FI — feedback is recorded but not corrective
high R_eff but low FI — repair capacity exists but feedback cannot direct it
high feedback volume and low FI — noise or theater overwhelms correction
low feedback volume and high FI — high-quality feedback is routed and acted on
high FI at U3 but low FI at U2/U4 — behavior adjusts but structure remains unchanged

What Changes Its Meaning

FI_integrity changes meaning under:

low EB
low signal_quality
low signal_localization_quality
low Au_eff
high Φ − O
high AP(t)
high Cv(t)
high X_c(t)
low R_eff
low M_int(t)
short τ_m(t)
high rank asymmetry
high dependency_load
high exit_cost
high U8 pressure
public legitimacy pressure

Context Modifiers

Low EB: feedback may never appear.

Low signal_quality: feedback may need validation before action.

Low localization: feedback may be real but misrouted.

Low Au_eff: feedback outcomes cannot be traced.

High Φ−O: feedback may be filtered to preserve metrics.

High AP(t): feedback may collapse into blame conflict.

High X_c(t): procedural burden may block feedback.

Low R_eff: feedback may reveal problems that cannot be repaired.

Rank asymmetry: some feedback can correct the system more easily than other feedback.

Domain Calibration Notes

FI_integrity should be calibrated by domain:

in engineering: bug reports, incident learnings, postmortems, test failures, user reports
in AI: user feedback, eval failures, memory corrections, tool errors, safety reports
in institutions: complaints, audits, staff signal, affected-node reports, service outcomes
in governance: public testimony, oversight, court findings, audit results, civic feedback
in relationships: boundary feedback, repair feedback, repeated pattern naming, trust signals
in archives: correction notes, reader confusion, glossary drift reports, cross-link errors, source challenges

11) Operator Sequencing Implications

If FI_integrity Is Healthy

Allowed with ordinary gate checks:

Μ can use feedback for sensemaking
Γ can select repair priorities from feedback
Π can redesign constraints from feedback
ℛ can repair based on feedback
Δ can test whether feedback integration works
U7 memory can store feedback and correction history
Φ can remain useful if feedback can falsify it

Recommended:

feedback → Au trace → Μ interpret → Γ prioritize → ℛ repair → U7 update → recurrence validation

If FI_integrity Is Low

Recommended:

pause feedback-based closure → restore EB/Au route → protect affected-node signal → reconnect feedback to action → repair memory loop

Or:

treat feedback metrics as provisional → seek direct signal → test whether feedback changes behavior

Avoid or delay:

claiming responsiveness
declaring repair complete
scaling based on satisfaction metrics
using feedback volume as legitimacy
U7 closure memory
hard Γ from sanitized feedback
punishing repeated feedback
treating low complaint rate as coherence

Operators Recommended Under Low FI_integrity

Ψ: attend directly to feedback that may be filtered
Au: trace feedback-to-action pathway
Μ: reinterpret feedback without confirmation bias
Θ: damp defensiveness and certainty
Ξ: detect feedback theater or pseudo-learning
Π: protect channels and require correction linkage
Γ: select feedback system repairs
ℛ: repair the feedback loop before relying on it

Operators Contraindicated Under Low FI_integrity

Γ hard selection: may select from filtered feedback
Π irreversible constraint: may encode sanitized signal
⊗ deep coupling: may deepen dependence without correction ability
⊕ composition: may embed feedback failure into new identity
Τ acceleration: outruns correction
Σ escalation: may sacralize feedback-resistant claims
✕ force: suppresses feedback and creates repair debt

12) Gate Implications

Gates Strengthened By Reliable FI_integrity

FI-Gate: direct diagnostic support for gate pass/fail
Au-Actuation: feedback path can be traced into action
HR-Gate: feedback can revise classifications before identity binding
MS-Gate: checks whether feedback access and effect are symmetrical
☷ᵢ: principles can remain correctable by reality contact

Gates Weakened If FI_integrity Is Poor or Unknown

If FI_integrity is low:

FI-Gate should fail or require repair
Au may preserve reports without correction
HR may bind labels despite feedback
MS may miss whose feedback matters
☷ᵢ may become rhetorical or unfalsifiable
Π may block inconvenient feedback
Γ may select from sanitized reality
ℛ may perform repair theater

Gate Outcomes Affected

Low FI_integrity should push gates toward:

Pause closure
Repair feedback pathway
Require source-to-action trace
Require affected-node feedback
Require recurrence validation
Deny feedback-based success claims
Deny repair-complete memory
Deny scaling from sanitized metrics
∅ for high-impact action when feedback cannot falsify the system

13) Scaling Behavior

FI_integrity becomes harder under scale because feedback travels through layers, summaries, dashboards, incentives, public narratives, and authority filters.

As systems scale:

feedback volume increases
feedback is summarized
affected-node signal compresses
dashboards replace direct signal
inconvenient feedback is filtered
response becomes procedural
low-status feedback loses force
feedback becomes metricized
public feedback and actual feedback diverge
feedback-to-action pathways lengthen
correction becomes slower
feedback is collected after decisions
recurrence data is renamed
U7 stores closure rather than correction

Scaling Risks

feedback theater
pseudo-learning
Goodhart feedback loops
sanitized reporting
affected-node invisibility
complaint exhaustion
public/private feedback divergence
dashboard blindness
correction lag
unfalsifiable governance
repair theater
legitimacy shock
memory contamination
rank-asymmetric feedback power
false satisfaction signals

Scaling Requirements

To scale FI_integrity safely, systems need:

source-to-action traceability
affected-node pathways
feedback outcome tracking
independent feedback channels
contradiction preservation
feedback-to-repair linkage
feedback-to-memory linkage
anti-retaliation / cost reduction where needed
signal quality checks
localization checks
rank-symmetry review
feedback_action_ratio tracking
recurrence validation
public/private signal comparison
feedback de-sanitization
closure criteria based on behavior, not collection

Scaling Rule

Feedback integrity must scale with system consequence, coupling depth, proxy pressure, and memory durability.

Sanity constraint:

High Φ pressure + low FI_integrity ⇒ Goodhart_risk ↑

If the system is optimizing a proxy and feedback cannot falsify it, Goodhart risk rises.

Second constraint:

Feedback collected − feedback acted upon ⇒ feedback theater risk ↑

If feedback volume rises without behavioral correction, the feedback system may be performative.

Third constraint:

Low FI_integrity + durable U7 closure ⇒ false repair memory risk ↑

If feedback cannot correct closure claims, memory may store repair as complete when recurrence remains.

14) Interaction / Coupling Behavior

FI_integrity reveals whether a relation, institution, AI system, archive, or interface can receive correction without collapsing, filtering, or converting it into confirmation.

What It Reveals About Coupling

whether one node can correct another
whether feedback flows both ways
whether affected-node feedback matters
whether feedback changes future interaction
whether coupling becomes feedback-resistant
whether one node absorbs feedback burden
whether disagreement can preserve connection
whether repair can be guided by reality rather than preferred narrative

What It Reveals About Boundary Integrity

Boundary integrity requires feedback.

When FI_integrity is low:

boundary strain may not change behavior
refusal may be heard but not honored
consent ambiguity may remain unresolved
boundary repair may be claimed but not validated
repeated feedback becomes burden on the affected node
BΣ degradation may continue beneath apparent acknowledgment

What It Reveals About Compatibility

Compatibility requires correction capacity.

A coupling may be unsafe if:

one node can provide feedback but the other cannot be changed by it

or:

feedback is allowed only when it supports the existing trajectory

Healthy compatibility requires feedback that can revise behavior, meaning, memory, and constraints.

Relevant Interface Acts

↺ Reflection: checks whether feedback was received accurately
⇩ Relaxation: lowers defensiveness so feedback can land
⊘ Attenuation: reduces coupling when feedback cannot correct harm
⊙ Alignment: self-corrects before demanding external feedback
→? Invitation: invites feedback without forcing agreement
⚕︎ Restorative Override: requires post-action feedback validation
✕ Force: usually collapses feedback integrity and creates debt

15) Failure Modes Detected

Primary Failure Modes

FI_integrity detects or predicts:

feedback theater
pseudo-learning
Goodhart risk
dashboard blindness
sanitized reporting
affected-node invisibility
repair theater
complaint exhaustion
feedback suppression
false responsiveness
correction lag
feedback-to-action break
feedback memory failure
recurrence despite reports
public/private feedback divergence
rank-asymmetric correction power
unfalsifiable system narrative

Composite Regimes Where FI_integrity Matters

Goodhart Collapse: feedback cannot falsify Φ
Crisis Loop: repeated feedback does not change recurrence
Pseudo-Coherent Basin: feedback is absorbed without altering hidden debt
Repair Theater: feedback is acknowledged but not repaired
Mission Lock: feedback that challenges trajectory is filtered
Taboo Lock: certain feedback cannot be expressed or acted on
Coercive Fusion: one node’s feedback is overwritten by relation maintenance
LOS: latent operations ignore formal feedback
Compression Collapse: feedback is compressed before it can correct

16) Accountability & Reintegration Implications

If FI_integrity Was Ignored

Likely consequences:

feedback was collected but not used
affected-node signal was filtered
repair claims were not falsifiable
recurrence persisted despite reports
metrics improved while reality degraded
memory stored false closure
trust in feedback channels collapsed
hidden debt accumulated
the system appeared responsive while becoming less correctable

Accountability questions:

Who gave feedback?
Where did it go?
Who could act on it?
Did behavior change?
Did memory update?
Did recurrence decline?
Was feedback summarized or distorted?
Was inconvenient feedback filtered?
Did feedback threaten Φ?
Did low-rank feedback have the same force as high-rank feedback?
Was feedback used to repair or to validate existing claims?

If FI_integrity Was Misread

Possible misread forms:

feedback collection mistaken for feedback integration
acknowledgment mistaken for correction
low complaint rate mistaken for satisfaction
high feedback volume mistaken for reality contact
feedback metrics mistaken for feedback integrity
rejected feedback mistaken for suppression when quality was low
careful feedback sorting mistaken for avoidance
delayed correction mistaken for non-response
surface change mistaken for root correction

Required Restoration

When FI_integrity failure is found:

recover feedback history
→ trace feedback-to-action path
→ identify filtering/distortion points
→ restore affected-node access
→ reconnect feedback to decision/repair authority
→ update U7 memory
→ validate recurrence reduction

If feedback force was asymmetric, MS-Gate should review whose feedback could change the system and whose could not.

17) Cross-Domain Examples

Technical / Engineering

Teams repeatedly report the same fragile subsystem, but prioritization metrics keep deferring the repair.

Diagnostic implication: feedback exists but cannot override Φ-driven prioritization.

Operator sequence: feedback-to-action audit → Φ−O review → Γ repair priority reset → ℛ subsystem repair → U7 postmortem update.

Institutional / Governance

A complaint system receives many reports, but cases are closed through procedural acknowledgment without correcting the underlying process.

Diagnostic implication: feedback channel exists; feedback integrity is low.

Operator sequence: source-to-action trace → affected-node validation → MS review → Π process repair → recurrence tracking.

AI / Algorithmic

Users flag a recurring AI failure, but the feedback is reduced to narrow categories and does not update evaluation, tool routing, memory, or model behavior.

Diagnostic implication: feedback is compressed before it can correct.

Operator sequence: feedback taxonomy repair → raw feedback preservation → Γ eval update → ℛ tool/model/memory repair → Δ regression test.

Interaction / Relational

One person repeatedly names a boundary issue. The other acknowledges it but behavior does not change.

Diagnostic implication: expression and acknowledgment exist, but feedback-to-action linkage is weak.

Operator sequence: ↺ reflection → identify behavior change → Π boundary repair → ℛ recurrence tracking → Λ re-test.

Archive / Framework Design

Readers flag definition drift, but the archive keeps producing new documents without updating glossary, cross-links, or prior specs.

Diagnostic implication: feedback enters but does not update U7 archive memory.

Operator sequence: correction queue → glossary repair → cross-link update → U7 version history → Δ reader stress-test.

18) Test Protocols

1. Feedback-to-Action Test

Does feedback change behavior, constraints, repair, or memory?

Failure signal: feedback is collected but not consequential.

2. Falsification Test

Can feedback disprove preferred outcomes or success claims?

Failure signal: feedback only confirms the system.

3. Affected-Node Test

Can affected nodes provide feedback that reaches repair authority?

Failure signal: feedback is filtered before authority sees it.

4. Source-to-Summary Test

Does feedback preserve source context through summarization?

Failure signal: feedback becomes sanitized or generic.

5. Recurrence Test

Does recurrence decline after feedback integration?

Failure signal: same issue returns after feedback is “addressed.”

6. Memory Update Test

Does feedback update U7 memory?

Failure signal: feedback is forgotten after closure.

7. Rank Symmetry Test

Does feedback from different ranks/nodes have comparable correction power?

Failure signal: some feedback changes the system, some disappears.

8. Proxy Challenge Test

Can feedback that harms Φ still be heard?

Failure signal: feedback is accepted only when metric-compatible.

9. Closure Challenge Test

Can feedback reopen closure?

Failure signal: once closed, feedback can no longer affect repair status.

10. Feedback Quality Sorting Test

Can the system distinguish low-quality feedback from inconvenient high-quality feedback?

Failure signal: rejection criteria are unclear or biased.

19) Anti-Patterns

Feedback channel as feedback integrity
Survey as correction
Acknowledgment as action
Complaint closure as repair
Dashboard as feedback
Low complaint rate as alignment
High feedback volume as reality contact
Feedback collected after closure
Feedback that cannot change constraints
Feedback routed away from authority
Sanitized summaries as affected-node signal
Dissent as noise
Feedback threatening Φ ignored
Feedback acted on only when easy
Recurrence after “feedback addressed”
Public response as repair
Feedback as legitimacy theater
Listening session as restoration
Internal metrics as affected-node truth
Feedback memory without behavior change

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

FI_integrity Feedback Integrity is the diagnostic estimate of whether feedback can accurately enter the system, remain connected to reality, challenge preferred outcomes, alter behavior, update memory, and trigger correction. It differs from EB and Au_eff: EB asks whether signal can be expressed, Au_eff asks whether signal can be traced, and FI_integrity asks whether feedback can actually falsify and correct the system. Low FI_integrity indicates risk of feedback theater, pseudo-learning, Goodhart risk, sanitized reporting, repair theater, affected-node invisibility, recurrence despite reports, false responsiveness, and memory contamination. Under low FI_integrity, feedback-dependent closure, responsiveness claims, scaling from feedback metrics, and repair-complete memory should pause while the system restores source-to-action traceability, affected-node access, correction authority, feedback-to-memory linkage, and recurrence validation.