1) Diagnostic Identity

Diagnostic Name: Selection Traceability

Short Name / Symbol: selection_traceability

Diagnostic Class: Selection / Auditability / Decision Provenance / Γ Integrity / Memory Safety

Primary Function: Estimate whether a system can reconstruct why a selection was made, what alternatives were considered, what criteria were used, what evidence supported the choice, what tradeoffs were accepted, and what risks were deferred.

Primary Use: Determine whether Γ selection is auditable enough to support repair, revision, accountability, learning, and future re-selection.

Core Risk if Ignored: The system may select, standardize, canonize, approve, reject, or commit without preserving enough rationale to later understand, repair, reverse, or improve the decision.

Core Risk if Overtrusted: A well-documented selection may be mistaken for a coherent selection, even when the evidence, criteria, assumptions, or rejected-option review were poor.

2) Mechanical Definition

selection_traceability measures whether a selection pathway remains reconstructible after the decision is made.

selection_traceability answers:

Can we tell why this option was selected instead of the alternatives?

A selection may involve:

choosing a design
choosing a policy
choosing a repair path
choosing a label
choosing a metric
choosing a partner
choosing a canon status
choosing an interpretation
choosing a constraint
choosing a tool
choosing a trajectory

Selection Traceability is not simply documentation volume.

It asks whether the selection record preserves the parts needed for future audit:

options considered
options rejected
selection criteria
evidence used
uncertainty level
constraints active
tradeoffs accepted
risks deferred
affected-node input
decision authority
timing pressure
review conditions
revision pathway

A system with low selection_traceability can still make good choices by chance, but it cannot reliably learn from them, repair them, or revise them.

A simple rule:

untraceable selection becomes unrepairable selection

3) What the Diagnostic Measures

Direct Measurement Target

selection_traceability measures:

• decision provenance
• option history
• rejected-option record
• selection criteria clarity
• evidence lineage
• confidence / uncertainty record
• tradeoff visibility
• risk acknowledgment
• constraint conditions during selection
• timing pressure during selection
• who selected
• who was excluded from selection
• what feedback entered selection
• what metrics shaped selection
• whether selection can be reviewed
• whether selection can be reversed or revised
• whether future recurrence can be linked back to selection

Indirect / Proxy Signals

selection_traceability can be estimated from:

• existence of decision logs
• quality of rationale
• option comparison records
• rejected-option archive
• source-to-decision linkage
• whether selection criteria are explicit
• whether affected-node input is recorded
• whether uncertainty was preserved
• whether tradeoffs were named
• whether selection was made under pressure
• whether decision authority is known
• whether review date exists
• whether changes in evidence update the selection
• whether later failure can be traced back to the selection
• whether similar future decisions reuse the rationale accurately
• whether selected path becomes canon without source context

What It Does Not Measure

selection_traceability does not directly measure:

• whether the selection was correct
• whether the selected option was best
• whether the rejected options were low quality
• whether the evidence was sufficient
• whether the selection was fair
• whether the chosen option will work
• whether the selection should remain permanent
• whether all options were known
• whether a well-traced decision is coherent
• whether a poorly traced decision is necessarily wrong

High selection_traceability means the decision pathway can be audited and learned from.

It does not guarantee selection quality.

Low selection_traceability means the decision pathway is hard to reconstruct.

It does not prove the selection was bad, but it makes repair, accountability, and revision harder.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

• Au: selection traceability is a direct expression of auditability at the Γ layer
• O: coherent selection requires future review against actual coherence effects
• H: hidden debt rises when untraceable choices create deferred consequences
• µᵢ: integrity depends on preserving the link between model, choice, action, and consequence
• R: repair depends on knowing why a path was selected
• Φ: proxy-driven selection must be traceable to detect metric capture

Secondary Variables

• ε: visible errors after selection may require reconstruction of the decision
• ι: pseudo-coherence can arise when selected path appears inevitable because alternatives are forgotten
• BΣ: boundary effects of a selection must be traceable
• K: compatibility decisions require traceable evidence of fit

Variables Commonly Confused With selection_traceability

5) Localization Signature

Primary Legibility Layers

• U4 — Classification / Metrics / Narratives: where options are interpreted, scored, justified, narrated, and selected
• U5 — Coordination / Time: where timing, pressure, sequencing, approvals, and review windows shape selection
• U7 — Memory / Recurrence: where decision rationale becomes durable memory or disappears
• U6 — Coherence Field: where selected path produces whole-system coherence or hidden debt
• U2 — Configuration / Boundaries: where selected constraints, roles, permissions, and gates are encoded
• U3 — Execution: where selected path becomes action, implementation, or behavior

Primary Leverage Layers

• U4: preserve decision criteria, evidence, classification rationale, and tradeoffs
• U5: record timing, pressure, review windows, and decision sequencing
• U7: store selection memory with source, uncertainty, and revision pathway
• U2: ensure selected constraints remain linked to rationale
• U3: connect implementation outcomes back to selected choice

Verification Layers

• U4: can the selection criteria be reconstructed?
• U5: can timing and decision pressure be reconstructed?
• U7: does memory preserve options, rationale, and review path?
• U6: can outcomes be compared to the selection rationale?
• U3: can implementation be traced to the selected option?
• U2: can boundary/constraint effects be traced to selection?

Common Mislocalizations

• Treating final decision as full rationale
• Treating documentation volume as traceability
• Treating selected option as self-justifying
• Treating rejected options as irrelevant after decision
• Treating outcome success as proof of selection quality
• Treating authority approval as rationale
• Treating consensus as evidence
• Treating metric score as complete selection basis
• Treating canon status as decision provenance
• Treating old selection as current fit
• Treating inability to reconstruct as proof there were no alternatives
• Treating untraceable tradeoffs as nonexistent

6) Input Requirements

Required Inputs

To estimate selection_traceability, the system needs:

• selected option
• decision context
• available alternatives
• rejected options
• selection criteria
• evidence used
• decision authority
• affected variables in S
• confidence level
• uncertainty level
• tradeoffs accepted
• risks deferred
• constraints active during selection
• timing pressure
• affected-node input
• review or revision pathway
• memory storage location

Optional Inputs

These improve precision:

• decision logs
• meeting notes
• option scoring matrix
• source evidence
• rejected-option archive
• dissent records
• affected-node feedback
• metric lineage
• prior similar decisions
• stress-test results
• post-decision outcomes
• approval records
• version history
• public/private rationale comparison
• implementation trace
• review date
• future trigger conditions
• rollback path
• external audit record

Missing Input Behavior

If selection_traceability inputs are missing:

• If rejected options are missing, evaluate option loss risk before closure
• If criteria are missing, selection rationale is weak
• If evidence lineage is missing, confidence should be lowered
• If tradeoffs are missing, hidden debt may be unacknowledged
• If decision authority is unknown, accountability is unclear
• If timing pressure is unknown, compression effects may be hidden
• If affected-node input is missing, selection field is under-sampled
• If review path is missing, selected path may harden into unreviewable memory

Default missing-input posture:

treat selection as provisional → reconstruct options/criteria/evidence → preserve rationale → set review trigger

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

The selection pathway is reconstructible, evidence-linked, option-aware, and reviewable.

Signals:

• options considered are recorded
• rejected options are preserved with rationale
• selection criteria are explicit
• evidence lineage is traceable
• tradeoffs are named
• uncertainty is visible
• affected-node input is recorded where relevant
• decision authority is clear
• review triggers exist
• outcomes can be compared against rationale
• U7 memory preserves context and scope

Recommended posture:

proceed with selected path
store selection memory
monitor outcomes
review under stress, recurrence, or changed conditions

Watch Range

Selection is partially traceable, but some rationale, rejected-option history, uncertainty, or review path is weak.

Signals:

• selected option is documented but alternatives are thin
• evidence is available but scattered
• tradeoffs are implicit
• uncertainty is not clearly marked
• affected-node input is partial
• review window is vague
• rationale depends heavily on metrics
• timing pressure is not recorded
• rejected options may be hard to recover

Recommended posture:

strengthen decision record
recover rejected options
add uncertainty and review triggers
avoid irreversible binding

Degraded Range

Selection is difficult to reconstruct, making repair, reversal, or learning unreliable.

Signals:

• no clear selection criteria
• rejected options are lost
• authority approval substitutes for rationale
• metrics are cited without scope
• tradeoffs are unknown
• decision was made under pressure but not recorded
• affected-node input is absent
• selected path has become memory without provenance
• later recurrence cannot be linked to selection
• no review pathway exists

Recommended posture:

pause hard closure
reconstruct decision provenance
restore option memory
review selected path against outcomes
repair U7 decision memory

Contraindicated:

canonization
irreversible Π
scaling selected path
deleting alternatives
public certainty
repair-complete claims from untraceable decision

Critical / Collapse-Prone Range

Selection is untraceable but has become durable, high-consequence, or identity/canon-bound.

Signals:

• selected path governs major outcomes
• no one can explain why it was chosen
• alternatives are unrecoverable
• hidden debt accumulates but cannot be traced
• official memory treats selection as inevitable
• changing path would destabilize identity, canon, or legitimacy
• recurrence continues but selection cannot be reviewed
• decision authority is obscured
• external audit is required to reconstruct rationale

Recommended posture:

freeze selection-dependent expansion
preserve remaining evidence
activate Au / Ξ review
reconstruct options and criteria
downgrade durable memory if needed
create revision pathway

False Positive Risk

selection_traceability may appear low when:

• selection occurred through embodied expertise that can still be reconstructed
• rapid emergency selection was later documented
• rejected options were obvious but not written down
• low-stakes selection did not require full audit
• rationale is distributed across multiple artifacts
• the selected path is intentionally provisional
• high uncertainty was acknowledged in action rather than notes

False Negative Risk

selection_traceability may appear high when:

• documentation exists but does not preserve real criteria
• rationale was written after the decision
• rejected options are straw-manned
• metrics are documented but misleading
• tradeoffs are omitted
• authority preference is hidden behind analysis
• dissent is excluded from record
• decision logs preserve narrative, not evidence
• U7 memory stores polished rationale rather than actual selection pathway

8) Leading Indicators

selection_traceability degradation appears early as:

• decisions are made before criteria are written
• rejected options are not recorded
• rationale is reduced to “best fit”
• metric scores replace explanation
• tradeoffs are not named
• dissent is not captured
• affected-node input is skipped
• timeline pressure is omitted
• review triggers are not set
• decision memory is scattered
• selected path is implemented before rationale is finalized
• alternatives are described vaguely
• public explanation is cleaner than internal uncertainty
• canon status appears before provenance

9) Lagging Indicators

selection_traceability failure has already accumulated debt when:

• system cannot explain why a path was chosen
• selected path fails and alternatives are lost
• repair cannot identify selection error
• recurrence is treated as new problem
• external audit is needed
• official memory invents rationale
• legitimacy shock follows decision reconstruction
• selected path becomes unchallengeable
• hidden tradeoffs surface later
• affected nodes reject the decision record
• the system cannot distinguish bad selection from bad execution
• option recovery becomes expensive or impossible

10) Interpretation Rules

How to Read selection_traceability

selection_traceability should be read as:

reconstructibility of the selection pathway

It is not a measure of decision correctness.

A system may have:

• high traceability and poor selection
• low traceability and good selection by chance
• high documentation and low traceability
• low documentation and recoverable rationale
• strong traceability for selected option and weak traceability for rejected options
• strong traceability for criteria and weak traceability for tradeoffs
• strong traceability before implementation but weak U7 memory later

What Changes Its Meaning

selection_traceability changes meaning under:

• high consequence severity
• high irreversibility
• high memory_binding_risk
• low classification_reversibility
• high rejected_option_quality
• low variance_preserved
• high innovation_exit
• high Φ pressure
• high Cv(t)
• low Au_eff
• low M_int(t)
• weak FI_integrity
• high stress_divergence
• canonization
• automation

Context Modifiers

High consequence severity: traceability threshold rises.

High irreversibility: option history and tradeoffs become critical.

High memory-binding risk: selection rationale must be preserved before U7 binding.

High rejected-option quality: rejected-option trace is especially important.

Low variance_preserved: lost alternatives must be documented.

High Φ pressure: proxy-driven selection must be auditable.

High Cv(t): compression may have narrowed options too quickly.

Canonization: selection trace must be strong before status hardens.

Domain Calibration Notes

selection_traceability should be calibrated by domain:

• in engineering: architecture decisions, incident fixes, deployment choices, vendor/tool selection
• in AI: model/tool/policy/eval/memory selections, safety classification decisions
• in institutions: policy decisions, hiring/promotion, complaint outcomes, program design
• in governance: legal, regulatory, budgetary, enforcement, and public-service decisions
• in relationships: repair path selection, boundary agreements, timing choices, role changes
• in archives: canon status, terminology, diagnostic inclusion, module structure, cross-link architecture

11) Operator Sequencing Implications

If selection_traceability Is Healthy

Allowed with ordinary gate checks:

• Γ selection can proceed
• Π constraints can be applied from the selected rationale
• ℛ can repair or revise based on trace
• U7 memory can store selection with provenance
• Δ can test selected path against assumptions
• Τ can proceed with review triggers
• canonization may proceed if other diagnostics pass

Recommended:

Γ select → preserve options/criteria/evidence → implement → monitor outcomes → U7 review trigger

If selection_traceability Is Low

Recommended:

pause durable commitment → reconstruct options and criteria → preserve rejected options → add uncertainty and review path

Or:

treat selection as provisional until rationale and tradeoffs are recovered

Avoid or delay:

• canonization
• irreversible Π
• public certainty
• scaling selected path
• deleting alternatives
• hard Γ closure
• automation
• durable U7 binding
• repair-complete claims based on selected path

Operators Recommended Under Low Traceability

• Au: reconstruct decision pathway
• Μ: rebuild selection sensemaking
• Γ: reselect if needed
• Θ: damp confidence in selected path
• FI: allow feedback to reopen selection
• ℛ: repair decision memory
• Δ: test selected assumptions
• Π: limit consequence until trace is restored

Operators Contraindicated Under Low Traceability

• Γ hard selection: may lock opaque choice
• Π irreversible constraint: encodes untraceable decision
• ⊕ composition: embeds unknown selection debt
• Τ acceleration: scales unreconstructible choice
• Σ escalation: sacralizes opaque selection
• ✕ force: enforces selection without review path
• ⊗ deep coupling: propagates selection debt

12) Gate Implications

Gates Strengthened By Reliable selection_traceability

• Au-Actuation: selection pathway can be inspected
• FI-Gate: feedback can challenge and revise selection
• High Risk Gate: blocks high-risk binding from opaque selection
• MS-Gate: checks whether options from different nodes were evaluated symmetrically
• ☷ᵢ: verifies that selected path preserves principle constraints

Gates Weakened If selection_traceability Is Poor or Unknown

If selection traceability is low:

• Au cannot reconstruct decision
• FI cannot target correction effectively
• High Risk Gate may allow binding from opaque criteria
• MS may miss asymmetric option evaluation
• ☷ᵢ may be invoked without knowing tradeoffs
• Π may encode unknown assumptions
• Γ may select from hidden criteria
• ℛ may repair execution instead of selection error

Gate Outcomes Affected

Low selection_traceability should push gates toward:

• Pause durable commitment
• Require decision provenance
• Require rejected-option record
• Require criteria/evidence map
• Require tradeoff record
• Require review trigger
• Deny canonization
• Deny irreversible actuation
• ∅ for high-impact selection whose rationale cannot be reconstructed

13) Scaling Behavior

selection_traceability becomes more important under scale because selected paths become infrastructure, policy, canon, automation, memory, or dependency.

As systems scale:

• selected path hardens
• alternatives disappear
• selection rationale decays
• metrics replace memory of criteria
• rejected-option memory is lost
• tradeoffs become hidden debt
• decision authority becomes unclear
• implementation teams inherit opaque choices
• automation repeats the selection
• public narrative replaces selection record
• reversing selection becomes costly
• future conditions expose unknown assumptions
• official memory treats selection as inevitable

Scaling Risks

• selection debt
• opaque canon
• policy fossilization
• automation of hidden assumptions
• option amnesia
• unrepairable decision
• external audit dependence
• legitimacy shock
• bad precedent
• hidden tradeoff accumulation
• metric capture
• brittle scaling
• irreversible misselection
• narrative replacing provenance
• repair mislocalization

Scaling Requirements

To scale selection safely, systems need:

• decision logs
• option inventory
• rejected-option records
• criteria maps
• evidence lineage
• confidence levels
• tradeoff records
• assumption records
• affected-node input
• review triggers
• rollback pathways
• version history
• post-decision audits
• selection-to-outcome tracking
• canon decision provenance
• automation assumption documentation

Scaling Rule

Selection traceability must scale with consequence severity, irreversibility, automation, canon authority, and dependency depth.

Sanity constraint:

selection_durability > selection_traceability ⇒ selection_debt ↑

If a selection becomes more durable than its traceability supports, selection debt rises.

Second constraint:

high rejected_option_quality + low selection_traceability ⇒ adaptive loss risk ↑

If strong alternatives were rejected but not recorded, adaptive loss risk rises.

Third constraint:

automation × low selection_traceability ⇒ hidden assumption propagation ↑

If automated systems repeat opaque selections, hidden assumptions spread.

14) Interaction / Coupling Behavior

selection_traceability reveals whether a relation, institution, archive, AI system, or interface can understand how it chose its current path.

What It Reveals About Coupling

• whether shared commitments have known rationale
• whether one node’s criteria dominated selection
• whether alternatives from weaker nodes were recorded
• whether coupling terms were chosen consciously
• whether rejected terms remain recoverable
• whether repair can revisit the original choice
• whether current path is continued from evidence or inertia
• whether compatibility claims are based on traceable selection

What It Reveals About Boundary Integrity

Selections often define boundaries.

When selection_traceability is low:

• boundary terms may harden without rationale
• refusal/permission structures may become arbitrary
• constraints may persist after purpose is forgotten
• BΣ repair becomes harder
• exit/renegotiation becomes difficult
• affected nodes may not know why the selected boundary exists

What It Reveals About Compatibility

Compatibility requires knowing why the coupling path was selected.

A coupling may be unsafe if:

the current form persists but no one can reconstruct why this form was chosen

or:

the selected relation/interface/path excluded alternatives that were never evaluated

Healthy compatibility includes the ability to revisit the original selection with evidence.

Relevant Interface Acts

• ↺ Reflection: reconstruct why the current path was selected
• ⇩ Relaxation: reduce pressure to defend the chosen path
• ⊘ Attenuation: reduce coupling while selection trace is repaired
• ⊙ Alignment: clarify one’s own selection criteria
• →? Invitation: invite alternative criteria before closure
• ⚕︎ Restorative Override: requires post-action selection trace repair
• ✕ Force: dangerous when the selection path is opaque

15) Failure Modes Detected

Primary Failure Modes

selection_traceability detects or predicts:

• opaque selection
• selection debt
• option amnesia
• hidden tradeoffs
• rejected-option loss
• selection narrative drift
• canon without provenance
• automation of hidden assumptions
• policy fossilization
• repair mislocalization
• bad precedent
• unreviewable standardization
• metric-driven selection without scope
• authority preference hidden as analysis
• legitimacy shock after rationale recovery
• irreversible misselection
• U7 memory contamination

Composite Regimes Where selection_traceability Matters

• Goodhart Collapse: selected path was chosen by proxy but rationale is hidden
• Mission Lock: selection becomes unreviewable trajectory
• Taboo Lock: selected option cannot be questioned
• Pseudo-Coherent Basin: current order hides forgotten alternatives
• Compression Collapse: selection made under pressure loses option trace
• Crisis Loop: recurring failure cannot be linked to bad selection
• Repair Theater: repair path selected without traceable cause
• Coercive Fusion: selected coupling persists without recoverable consent/rationale
• LOS: latent selection criteria govern beneath formal rationale

16) Accountability & Reintegration Implications

If selection_traceability Was Ignored

Likely consequences:

• decision could not be repaired
• rejected options were lost
• tradeoffs became hidden debt
• selected path became overtrusted
• future recurrence was mislocalized
• authority preference was hidden
• canon or policy hardened without provenance
• selected path scaled beyond its evidence
• official narrative replaced real rationale
• affected nodes could not contest the selection

Accountability questions:

• What was selected?
• What alternatives existed?
• Why were they rejected?
• What criteria were used?
• What evidence supported the choice?
• Who decided?
• Who was consulted?
• What tradeoffs were accepted?
• What risks were deferred?
• What review trigger was set?
• Did later outcomes validate or challenge the selection?
• Was selection memory accurate?

If selection_traceability Was Misread

Possible misread forms:

• sparse documentation mistaken for low traceability when rationale is reconstructible
• polished documentation mistaken for real traceability
• after-the-fact rationale mistaken for decision provenance
• consensus mistaken for selection logic
• authority approval mistaken for evidence
• metric score mistaken for full rationale
• missing rejected options mistaken for no alternatives
• high traceability mistaken for correctness
• low-stakes choices overburdened with excessive tracing

Required Restoration

When selection_traceability failure is found:

identify selected path
→ reconstruct options and criteria
→ recover evidence and tradeoffs
→ identify rejected-option loss
→ compare selected path to outcomes
→ repair U7 decision memory
→ create review and revision pathway

If selection traceability failure affected nodes asymmetrically, MS-Gate should review whose options, evidence, and objections were preserved or erased.

17) Cross-Domain Examples

Technical / Engineering

A system uses a legacy architecture, but no one can explain why it was selected or what alternatives were considered.

Diagnostic implication: low selection traceability creates repair and migration difficulty.

Operator sequence: architecture decision reconstruction → dependency map → rejected-option recovery → migration review.

Institutional / Governance

A policy is enforced for years, but the original evidence, tradeoffs, and affected-node input are no longer available.

Diagnostic implication: policy selection has become untraceable memory.

Operator sequence: policy provenance audit → affected-node review → criteria repair → revision pathway.

AI / Algorithmic

A model safety rule exists, but its original failure cases, evidence threshold, and rejected alternative rules are unknown.

Diagnostic implication: safety selection lacks U7 provenance.

Operator sequence: rule lineage audit → eval reconstruction → rejected-policy review → updated policy memory.

Interaction / Relational

A relationship pattern becomes “the way we do things,” but neither person can reconstruct when or why it was chosen.

Diagnostic implication: relational selection has become default memory without traceability.

Operator sequence: ↺ reflection → reconstruct origin → evaluate fit → renegotiate terms.

Archive / Framework Design

A diagnostic is marked “core,” but the archive no longer records why it was core rather than derived or local.

Diagnostic implication: canon status selection lacks traceability.

Operator sequence: canon provenance audit → status review → cross-link correction → U7 version update.

18) Test Protocols

1. Option Inventory Test

Can the system list what options were considered?

Failure signal: selected path exists without option history.

2. Rejection Rationale Test

Can the system explain why alternatives were rejected?

Failure signal: rejected options disappeared or were dismissed vaguely.

3. Criteria Test

Were selection criteria explicit?

Failure signal: selection appears preference-driven or retrospective.

4. Evidence Lineage Test

Can evidence be traced to source?

Failure signal: evidence is summarized without provenance.

5. Tradeoff Test

Were accepted tradeoffs named?

Failure signal: costs appear later as hidden debt.

6. Authority Test

Who made the selection?

Failure signal: decision authority is unclear or hidden.

7. Affected-Node Input Test

Were affected nodes included where relevant?

Failure signal: selected path imposes burden on excluded nodes.

8. Review Trigger Test

Was there a condition for revisiting the selection?

Failure signal: selected path hardens indefinitely.

9. Outcome Comparison Test

Do outcomes validate the selection rationale?

Failure signal: rationale and observed effects diverge.

10. Memory Test

Does U7 preserve selection with context?

Failure signal: selection becomes canon, policy, or habit without provenance.

19) Anti-Patterns

• Winner as rationale
• Authority approval as evidence
• Consensus as selection logic
• Metric score as full rationale
• Documentation volume as traceability
• After-the-fact justification as provenance
• Rejected options erased
• Tradeoffs unnamed
• Affected-node input omitted
• Review trigger absent
• Canon status without selection record
• Policy without origin memory
• Selected path as inevitable
• Alternatives as clutter
• Decision under pressure without pressure record
• Public narrative as decision history
• Old default as current fit
• Irreversible selection without trace
• Automation of opaque choice
• Repair blocked by missing rationale

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

selection_traceability is the diagnostic estimate of whether a system can reconstruct why a selection was made, what alternatives were considered, what criteria and evidence were used, what tradeoffs were accepted, what risks were deferred, who decided, and when the selection should be reviewed. It does not measure whether the selection was correct; it measures whether the decision can be audited, repaired, revised, or learned from. Low selection_traceability indicates risk of selection debt, option amnesia, hidden tradeoffs, opaque canonization, automation of hidden assumptions, repair mislocalization, unreviewable standards, and U7 memory contamination. Under low selection_traceability, the system should pause durable commitment, reconstruct options and criteria, preserve rejected-option memory, name tradeoffs and uncertainty, create review triggers, and avoid canonization, irreversible constraints, automation, or scaling selected paths until selection provenance is restored.