1) Diagnostic Identity

Diagnostic Name: Feedback–Action Ratio

Short Name / Symbol: feedback_action_ratio

Diagnostic Class: Feedback / Correction / Responsiveness / FI Support / Repair Throughput

Primary Function: Estimate how much feedback, signal, complaint, contradiction, report, audit finding, affected-node input, or review result is converted into actual system change, repair, correction, memory update, or constraint adjustment.

Primary Use: Determine whether feedback is merely being collected, acknowledged, summarized, or routed — or whether it meaningfully changes system behavior and reduces recurrence.

Core Risk if Ignored: The system may appear responsive because it receives or acknowledges feedback, while little or none of that feedback changes action, repair, constraints, memory, or future decisions.

Core Risk if Overtrusted: The system may optimize for visible action after feedback, even when the correct response is to verify, defer, reject, scope, or further investigate the signal before acting.

2) Mechanical Definition

feedback_action_ratio measures the conversion rate between feedback received and coherent action taken.

feedback_action_ratio answers:

Does feedback change what the system does?

Feedback can include:

complaints
reports
bug reports
audits
appeals
affected-node signal
reader confusion
boundary feedback
incident findings
stress-test results
user corrections
dissent
review findings
recurrence reports

Action can include:

behavior change
constraint repair
policy revision
memory correction
classification update
resource allocation
boundary repair
tool change
process repair
metric redesign
canon correction
repair escalation
review reopening

The diagnostic does not count all feedback as automatically actionable.

It asks whether valid, relevant, properly localized feedback is capable of producing proportionate change.

A simple form:

feedback_action_ratio = coherent action taken / actionable feedback received

A second form:

feedback heard but not acted on ⇒ FI theater risk ↑

3) What the Diagnostic Measures

Direct Measurement Target

feedback_action_ratio measures:

• feedback-to-action conversion
• feedback-to-repair conversion
• feedback-to-memory-update conversion
• feedback-to-classification-update conversion
• feedback-to-constraint-update conversion
• feedback-to-resource-allocation conversion
• feedback-to-boundary-repair conversion
• audit-finding-to-change conversion
• appeal-result-to-correction conversion
• affected-node-signal-to-repair conversion
• recurrence-report-to-system-change conversion
• whether feedback has operational consequence
• whether feedback can alter future decisions
• whether feedback reduces recurrence
• whether feedback changes U7 memory

Indirect / Proxy Signals

feedback_action_ratio can be estimated from:

• repeated feedback with no change
• feedback backlog
• feedback acknowledged but not assigned
• feedback assigned but not completed
• feedback summarized but not acted on
• response statements without behavior change
• audit findings repeated across cycles
• appeal wins without consequence repair
• bug reports closed as “known issue”
• complaint systems with low remedy rate
• affected-node feedback requiring repeated escalation
• recurrence after feedback was “addressed”
• feedback changing wording but not structure
• feedback used for metrics but not repair
• same issue returning in later reports
• feedback outcome not traceable

What It Does Not Measure

feedback_action_ratio does not directly measure:

• whether all feedback is valid
• whether all feedback should produce action
• whether fast action is always better
• whether every complaint should be accepted
• whether action quality is high
• whether feedback was correctly localized
• whether repair is complete
• whether action reduced hidden debt
• whether feedback was expressed coherently
• whether the system has enough resources to act
• whether rejected feedback was ignored unfairly

High feedback_action_ratio means actionable feedback is often converted into meaningful change.

It does not mean the system should obey all feedback.

Low feedback_action_ratio means feedback often fails to become action.

It does not prove bad faith; it may indicate weak capacity, poor routing, low review capacity, low R_eff, or unclear localization.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

• R: feedback must connect to restoration capacity to become action
• Au: feedback-to-action linkage must be traceable
• O: action should increase coherence, not merely satisfy feedback volume
• H: hidden debt rises when feedback identifies problems but action does not reduce them
• µᵢ: integrity depends on alignment between received feedback and changed behavior
• BΣ: boundary feedback must alter boundary conditions when valid

Secondary Variables

• ε: feedback often reports visible error or disturbance
• ι: pseudo-responsiveness appears when feedback is acknowledged but does not change the system
• K: coupling improves when feedback changes interaction
• Φ: systems may optimize for feedback-response metrics rather than actual repair

Variables Commonly Confused With feedback_action_ratio

5) Localization Signature

Primary Legibility Layers

• U3 — Execution: where feedback should alter behavior, tool use, process, service, action, or output
• U4 — Classification / Metrics / Narratives: where feedback is categorized, accepted, rejected, minimized, or reframed
• U5 — Coordination / Time: where feedback is assigned, sequenced, delayed, escalated, or closed
• U7 — Memory / Recurrence: where feedback should become learning, correction history, or recurrence prevention
• U6 — Coherence Field: where feedback-driven action either restores trust or reveals theater
• U2 — Configuration / Boundaries: where feedback may require rule, access, permission, or constraint repair

Primary Leverage Layers

• U3: connect feedback to execution changes
• U4: improve feedback classification and action criteria
• U5: create assignment, timing, and follow-through pathways
• U7: store feedback-action history and recurrence outcomes
• U2: change constraints and boundaries when feedback identifies structural causes
• U1: allocate resources for action

Verification Layers

• U3: did behavior or process change?
• U4: was feedback interpreted correctly?
• U5: was action assigned and completed?
• U7: did memory update and recurrence decline?
• U6: did trust/coherence improve?
• U2: did structural constraints change where needed?

Common Mislocalizations

• Treating feedback acknowledgment as action
• Treating response speed as correction
• Treating summary as integration
• Treating feedback collection as responsiveness
• Treating review as repair
• Treating apology as action
• Treating policy statement as behavior change
• Treating dashboard change as field change
• Treating closure status as resolution
• Treating low recurrence window as repair proof
• Treating repeated feedback as unreasonable instead of unacted-on
• Treating action count as action quality

6) Input Requirements

Required Inputs

To estimate feedback_action_ratio, the system needs:

• feedback set being evaluated
• feedback source
• feedback type
• actionability assessment
• evidence quality
• signal localization quality
• affected variables in S
• feedback destination
• action owner
• action taken or not taken
• action timing
• action consequence
• memory update status
• recurrence after action
• closure criteria
• affected-node validation
• feedback-to-action trace

Optional Inputs

These improve precision:

• feedback logs
• response logs
• action item records
• issue tracker data
• audit findings
• appeal results
• repair records
• policy change records
• memory correction records
• recurrence data
• time-to-action
• action completion rate
• feedback rejection reasons
• affected-node satisfaction or validation
• resource allocation data
• escalation records
• backlog age
• false-positive feedback history
• external audit comparison

Missing Input Behavior

If feedback_action_ratio inputs are missing:

• If actionability is unknown, do not count all feedback as ignored
• If action owner is missing, conversion is structurally weak
• If action status is unknown, feedback closure is unverified
• If memory update is unknown, learning may not persist
• If recurrence data is missing, action effectiveness is unvalidated
• If feedback rejection reasons are missing, rejection may be arbitrary or invisible
• If affected-node validation is missing, action may not have landed
• If traceability is low, reported conversion may be unreliable

Default missing-input posture:

classify feedback → assign owner → trace action → update memory → validate recurrence and affected-node state

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

Actionable feedback reliably becomes proportionate action, repair, memory correction, or constraint adjustment.

Signals:

• feedback is classified by actionability
• valid feedback receives owners
• actions are traceable
• closure criteria are explicit
• memory updates occur
• recurrence declines
• affected nodes validate improvement
• rejected feedback has rationale
• feedback outcomes are visible
• feedback changes future decisions

Recommended posture:

continue feedback loop
monitor recurrence
preserve feedback-action records
use outcomes to improve FI and ℛ

Watch Range

Feedback sometimes becomes action, but conversion is uneven, slow, or weakly validated.

Signals:

• feedback is acknowledged but action assignment is inconsistent
• action items exist but closure criteria are weak
• memory updates are partial
• recurrence data is incomplete
• affected-node validation is uneven
• feedback backlog grows slowly
• feedback changes surface behavior more than structure
• rejected feedback rationale is inconsistent

Recommended posture:

improve assignment and closure criteria
strengthen U7 memory update
track recurrence
reduce backlog
validate with affected nodes

Degraded Range

Feedback is frequently received but not converted into meaningful change.

Signals:

• repeated feedback with no system change
• acknowledgment without action
• feedback backlog grows
• audit findings repeat
• affected nodes escalate repeatedly
• action is superficial or symbolic
• recurrence persists after “addressed” status
• feedback is summarized away
• feedback cannot alter constraints or resources
• U7 memory does not update

Recommended posture:

pause responsiveness claims
activate FI / Au review
connect feedback to owners and repair authority
resource action pathway
reopen unresolved feedback

Contraindicated:

declaring responsiveness
closing feedback without action
repair-complete claims
punishing repeated feedback
scaling feedback collection
metricizing response without repair

Critical / Collapse-Prone Range

Feedback systems have become performative or legitimacy theater.

Signals:

• feedback cannot change outcomes
• reports are collected to absorb pressure
• repeated harms were already known
• affected nodes stop reporting
• external audit validates ignored feedback
• feedback channels lose legitimacy
• recurrence becomes normalized
• official memory stores closure despite unresolved feedback
• review and response exist but correction does not
• feedback becomes evidence of institutional failure

Recommended posture:

freeze feedback-based legitimacy claims
preserve ignored feedback
activate independent review
restore correction authority
repair affected-node burden
correct U7 false closure memory

False Positive Risk

feedback_action_ratio may appear low when:

• much feedback is low-quality or non-actionable
• feedback is being investigated carefully before action
• correct action is delayed by necessary evidence review
• feedback is contradictory and requires sorting
• action is happening at a deeper layer with delayed visibility
• feedback is valid but outside current scope
• immediate action would create more hidden debt
• repeated feedback is being addressed through long-cycle repair

False Negative Risk

feedback_action_ratio may appear high when:

• action is symbolic
• responses are counted as action
• closure status is counted as repair
• low-quality action does not reduce recurrence
• affected-node validation is missing
• memory does not update
• action changes wording but not behavior
• feedback is acted on only when easy
• high-volume trivial feedback inflates conversion metrics

8) Leading Indicators

feedback_action_ratio degradation appears early as:

• feedback receives polite replies but no owners
• action items are vague
• closure criteria are unclear
• same feedback recurs
• feedback is summarized into general themes
• affected nodes ask what changed
• response time improves while repair does not
• review meetings increase but actions do not
• memory updates are skipped
• action depends on volunteer effort
• hard feedback is delayed
• feedback that threatens Φ is deprioritized
• repeated feedback is labeled as complaining
• feedback backlog ages without triage

9) Lagging Indicators

feedback_action_ratio failure has already accumulated debt when:

• ignored feedback becomes crisis
• external audit reveals known issues
• affected nodes stop using feedback channels
• recurrence proves non-action
• trust in feedback systems collapses
• repair claims are rejected
• official memory must be corrected
• feedback history becomes legitimacy evidence against the system
• downstream harm persists despite prior reports
• feedback collection is viewed as theater
• action requires major structural rebuild

10) Interpretation Rules

How to Read feedback_action_ratio

feedback_action_ratio should be read as:

conversion of actionable feedback into coherent system change

It is not the same as responsiveness theater.

A system may have:

• high feedback volume and low action ratio
• low feedback volume and high action ratio
• high response rate and low action ratio
• high action count and low repair quality
• low action ratio because feedback quality is poor
• delayed action ratio because review is careful
• strong local action and weak U7 memory update
• strong U7 update and weak behavior change

What Changes Its Meaning

feedback_action_ratio changes meaning under:

• weak FI_integrity
• low review_capacity
• low attention_capacity
• low R_eff
• low Au_eff
• high affected_node_cost
• high recurrence
• high pseudo_damping_risk
• high Goodhart_risk
• high X_c(t)
• high coordination_overhead
• high resource_asymmetry
• high appeal_access_ratio demand
• low truth_tolerance
• high mission_lock_risk
• high L₀(t) degradation

Context Modifiers

Weak FI: feedback may not reach correction pathways.

Low review capacity: actionable feedback may wait too long.

Low attention capacity: feedback may be received but not processed.

Low R_eff: feedback may identify problems the system cannot repair.

Low Au_eff: feedback-to-action trace may be lost.

High affected-node cost: action urgency rises.

High recurrence: prior action did not land.

High Goodhart risk: response metrics may replace repair.

High coordination overhead: feedback action may stall in routing.

Mission lock: mission-inconvenient feedback may not act.

Domain Calibration Notes

feedback_action_ratio should be calibrated by domain:

• in engineering: bug reports to fixes, incident findings to architecture changes, postmortem actions to recurrence reduction
• in AI: user feedback to model/tool/memory/policy updates, eval findings to behavior changes
• in institutions: complaints to remedy, audits to reforms, staff feedback to process change
• in governance: public feedback to policy correction, court/audit findings to administrative change
• in relationships: feedback to behavior change, boundary signal to new practice, repair request to recurrence reduction
• in archives: reader confusion to glossary repair, review notes to spec edits, drift reports to cross-link correction

11) Operator Sequencing Implications

If feedback_action_ratio Is Healthy

Allowed with ordinary gate checks:

• FI feedback can guide Γ selection
• ℛ can use feedback for repair
• Π can adjust constraints based on signal
• U7 can store action history
• Τ can proceed with correction loops active
• Λ / K_real can improve through acted-on feedback
• public responsiveness claims may be made with evidence

Recommended:

feedback → actionability review → owner assignment → ℛ / Π action → U7 memory update → recurrence validation

If feedback_action_ratio Is Low

Recommended:

pause responsiveness claims → triage feedback → assign owners → resource action pathway → validate recurrence and affected-node recovery

Or:

separate feedback collection from correction power → repair the conversion layer

Avoid or delay:

• claiming responsiveness
• scaling feedback collection
• repair-complete claims
• closing reports without action
• punishing repeated signal
• using response metrics as proof
• durable closure memory
• public legitimacy claims from feedback volume

Operators Recommended Under Low feedback_action_ratio

• FI: restore correction pathway
• Au: trace feedback-to-action linkage
• Γ: prioritize actionable feedback
• ℛ: execute repair
• Π: change constraints and ownership structures
• Θ: damp defensiveness and closure pressure
• Ψ: attend to repeated signal
• Ξ: detect feedback theater

Operators Contraindicated Under Low feedback_action_ratio

• Γ closure selection: may close unresolved feedback
• Π process expansion: may create more theater without action
• Τ acceleration: outruns correction
• Σ escalation: may sacralize responsiveness narrative
• ✕ force: suppresses repeated feedback
• ⊗ deep coupling: increases feedback load without repair
• ⊕ composition: embeds uncorrected feedback failure

12) Gate Implications

Gates Strengthened By Reliable feedback_action_ratio

• FI-Gate: direct support for whether feedback has correction power
• Au-Actuation: feedback-to-action linkage is traceable
• High Risk Gate: blocks binding when feedback contradictions are not acted on
• MS-Gate: checks whose feedback becomes action
• ☷ᵢ: ensures principles are updated by lived feedback, not only asserted

Gates Weakened If feedback_action_ratio Is Poor or Unknown

If feedback-action conversion is low:

• FI may be performative
• Au may record feedback without consequence
• High Risk Gate may bind labels despite unresolved contradiction
• MS may miss whose feedback matters
• ☷ᵢ may become slogan rather than corrective principle
• Π may preserve structures that feedback has falsified
• Γ may select from stale reality
• ℛ may not reach recurring issues

Gate Outcomes Affected

Low feedback_action_ratio should push gates toward:

• Pause closure
• Require action owner
• Require feedback-to-action trace
• Require recurrence validation
• Require affected-node validation
• Deny responsiveness claims
• Deny repair-complete memory
• Deny scaling feedback collection
• ∅ for high-impact action when relevant feedback cannot alter the system

13) Scaling Behavior

feedback_action_ratio becomes harder under scale because feedback volume rises faster than review, action, memory, and repair capacity.

As systems scale:

• feedback volume increases
• feedback is summarized
• action ownership becomes unclear
• review queues grow
• low-status feedback loses force
• response metrics replace repair metrics
• hard feedback is deprioritized
• recurrence patterns are buried
• memory updates lag
• affected-node validation is compressed
• feedback collection becomes legitimacy performance
• action requires coordination across more layers
• trivial feedback inflates action metrics
• structural feedback becomes expensive to act on

Scaling Risks

• feedback theater
• responsiveness theater
• complaint exhaustion
• known-issue normalization
• action backlog
• repair backlog
• recurrence after feedback
• affected-node disengagement
• legitimacy shock
• Goodharted response metrics
• unacted audit findings
• feedback memory failure
• external audit exposure
• correction lag
• structural feedback suppression

Scaling Requirements

To scale feedback-action conversion safely, systems need:

• feedback triage
• actionability criteria
• owner assignment
• consequence-weighted priority
• repair resources
• review capacity
• memory update pathways
• recurrence tracking
• affected-node validation
• action closure criteria
• rejection rationale
• feedback-to-action dashboards
• structural feedback lanes
• hard-feedback escalation
• external audit triggers
• anti-Goodhart checks on response metrics

Scaling Rule

Feedback systems may scale only as far as action capacity, review capacity, memory update capacity, and repair capacity scale with them.

Sanity constraint:

feedback_volume > action_capacity ⇒ feedback_debt ↑

If feedback grows faster than action capacity, feedback debt accumulates.

Second constraint:

response_rate ↑ + action_rate↓ ⇒ responsiveness_theater risk ↑

If replies increase but action does not, theater risk rises.

Third constraint:

feedback_action_ratio↓ + recurrence↑ ⇒ repair_failure risk ↑

If feedback does not convert to action and recurrence rises, repair is failing.

14) Interaction / Coupling Behavior

feedback_action_ratio reveals whether a coupling can change when one node gives signal to another.

What It Reveals About Coupling

• whether feedback is consequential
• whether one node must repeat signal
• whether affected-node feedback changes behavior
• whether repair burden is one-sided
• whether feedback can update shared memory
• whether feedback alters boundaries
• whether coupling becomes more coherent over time
• whether feedback channels are trust-building or draining

What It Reveals About Boundary Integrity

Boundary integrity depends on feedback becoming action.

When feedback_action_ratio is low:

• boundary signals repeat
• refusal does not change behavior
• boundary repair is claimed but not acted on
• affected nodes stop reporting
• BΣ damage accumulates
• silence may replace correction
• exit may become the only remaining action

What It Reveals About Compatibility

Compatibility requires feedback responsiveness.

A coupling may be unsafe if:

one node can express feedback but the other does not change

or:

repair requires the affected node to repeat the same signal indefinitely

Healthy compatibility includes a feedback-action pathway that reduces recurrence.

Relevant Interface Acts

• ↺ Reflection: confirm what feedback requires
• ⇩ Relaxation: reduce defensiveness before action
• ⊘ Attenuation: reduce coupling if feedback remains unacted-on
• ⊙ Alignment: change one’s own behavior in response to valid feedback
• →? Invitation: invite feedback with clear action pathway
• ⚕︎ Restorative Override: requires post-action feedback validation
• ✕ Force: often suppresses feedback and lowers action integrity

15) Failure Modes Detected

Primary Failure Modes

feedback_action_ratio detects or predicts:

• feedback theater
• responsiveness theater
• complaint exhaustion
• known-issue normalization
• repeated feedback without change
• feedback backlog
• action backlog
• repair backlog
• recurrence after feedback
• affected-node disengagement
• symbolic response
• memory update failure
• unacted audit findings
• response metric Goodharting
• closure without action
• feedback-to-repair break
• legitimacy decline

Composite Regimes Where feedback_action_ratio Matters

• Repair Theater: feedback is acknowledged but not repaired
• Goodhart Collapse: response metrics replace action
• Crisis Loop: unacted feedback becomes recurring crisis
• Pseudo-Coherent Basin: feedback is absorbed without changing hidden debt
• Mission Lock: mission-inconvenient feedback is ignored
• Taboo Lock: protected topics cannot produce action
• Coercive Fusion: one node’s feedback cannot alter the coupling
• Extraction Regime: affected nodes supply feedback labor without repair
• LOS: actual action path bypasses formal feedback channel

16) Accountability & Reintegration Implications

If feedback_action_ratio Was Ignored

Likely consequences:

• feedback was collected but not acted on
• affected nodes repeated signal
• recurrence persisted
• feedback channels lost trust
• response metrics inflated responsiveness
• audit findings accumulated
• memory did not update
• repair-complete claims failed
• ignored feedback became crisis evidence
• external audit or exit became necessary

Accountability questions:

• What feedback was received?
• Was it actionable?
• Who owned action?
• What changed?
• When did it change?
• Did memory update?
• Did recurrence decline?
• Did affected nodes validate improvement?
• Was feedback rejected with rationale?
• Did response metrics hide non-action?
• Did the same feedback recur?

If feedback_action_ratio Was Misread

Possible misread forms:

• rejected low-quality feedback mistaken for ignored feedback
• careful investigation mistaken for inaction
• delayed structural repair mistaken for non-response
• symbolic first step mistaken for full action
• many small actions mistaken for meaningful repair
• action count mistaken for action quality
• feedback volume mistaken for action obligation
• recurrence during long repair mistaken for zero action
• action in deeper layers missed because visible behavior lags

Required Restoration

When feedback-action failure is found:

recover feedback history
→ classify actionable feedback
→ assign repair owners
→ connect feedback to action and memory update
→ reduce backlog
→ repair affected-node burden
→ validate recurrence reduction

If feedback-action conversion was asymmetric, MS-Gate should review whose feedback became action, whose was ignored, and who carried the cost of non-action.

17) Cross-Domain Examples

Technical / Engineering

Engineers repeatedly report a fragile subsystem. Leadership acknowledges the issue, but roadmap priorities never change.

Diagnostic implication: feedback is heard but not converted into action.

Operator sequence: feedback-to-action audit → assign owner → resource repair → recurrence tracking → U7 postmortem update.

Institutional / Governance

A complaint office sends polite responses, but complaints about the same process recur for years.

Diagnostic implication: response exists, action conversion is low.

Operator sequence: complaint cluster analysis → policy/process repair → affected-node validation → memory correction.

AI / Algorithmic

Users repeatedly correct a model’s memory, but the same incorrect assumption returns.

Diagnostic implication: user feedback is not updating U7 memory reliably.

Operator sequence: memory correction trace → recurrence test → user validation → memory boundary repair.

Interaction / Relational

One person gives the same boundary feedback repeatedly. The other acknowledges it each time, but behavior does not change.

Diagnostic implication: acknowledgment exists, feedback-action ratio is low.

Operator sequence: ↺ clarify required action → behavior change agreement → recurrence tracking → repair memory.

Archive / Framework Design

Readers flag glossary drift, but the archive keeps producing new modules without correcting the glossary.

Diagnostic implication: reader feedback is not converting into archive repair.

Operator sequence: feedback queue → glossary repair owner → cross-link update → U7 version note.

18) Test Protocols

1. Feedback Inventory Test

What feedback was received?

Failure signal: feedback cannot be listed or categorized.

2. Actionability Test

Was feedback actionable?

Failure signal: feedback is treated as noise without review.

3. Owner Test

Who owns the response action?

Failure signal: no accountable action owner exists.

4. Action Completion Test

Was any action taken?

Failure signal: feedback was acknowledged but no change occurred.

5. Memory Update Test

Did U7 update?

Failure signal: same feedback is rediscovered later.

6. Recurrence Test

Did action reduce recurrence?

Failure signal: issue returns after feedback was addressed.

7. Affected-Node Validation Test

Did the affected node experience improvement?

Failure signal: central action did not land locally.

8. Rejection Rationale Test

If feedback was rejected, why?

Failure signal: rejection is unexplained or arbitrary.

9. Structural Action Test

Did feedback alter cause-bearing structure?

Failure signal: action only changed wording or surface process.

10. Symmetry Test

Whose feedback becomes action?

Failure signal: conversion depends on rank, resource level, or status.

19) Anti-Patterns

• Acknowledgment as action
• Response as repair
• Feedback collection as responsiveness
• Review as action
• Summary as integration
• Closure status as resolution
• Apology as behavior change
• Policy statement as repair
• Known issue as managed issue
• Repeated feedback as complaining
• Response-time metric as correction
• Action count as action quality
• Feedback without owner
• Appeal win without repair
• Memory unchanged after correction
• Affected-node validation skipped
• Feedback rejected without rationale
• Feedback backlog as harmless
• Hard feedback delayed indefinitely
• Feedback used for legitimacy, not repair

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

feedback_action_ratio is the diagnostic estimate of how much actionable feedback, complaint, audit finding, affected-node signal, review result, appeal outcome, or recurrence report is converted into actual system change, repair, constraint adjustment, classification update, memory correction, or behavior change. It does not claim all feedback should produce action; it measures whether valid and relevant feedback can change the system. Low feedback_action_ratio indicates risk of feedback theater, responsiveness theater, repeated signal without repair, complaint exhaustion, action backlog, repair backlog, recurrence after feedback, memory update failure, response-metric Goodharting, and legitimacy decline. Under low feedback-action conversion, the system should pause responsiveness claims, triage feedback, assign owners, resource action pathways, trace feedback-to-action-to-memory, reduce backlog, repair affected-node burden, and validate recurrence reduction before repair-complete claims, durable closure memory, scaling feedback collection, or public legitimacy claims.