Regress Termination Rule

batch: First Iteration/Batch 42

slug: normativity

slug: closure

slug: satisficing

components:
Claim Under Justification
Regress Chain
Link Type Distinction
Foundational Assumption
Stopping Criterion
Sufficiency Threshold
Assumption Record
Reopening Condition
Burden of Reopening
Decision Scope Boundary
Uncertainty Residue
Authority Boundary
Timebox or Resource Limit
Counterparty Acceptance Test

mechanisms:
First-Principles Statement
Assumption Log
Burden-of-Proof Rule
Decision Closure Criteria
Evidence Sufficiency Rubric
Axiom Set
Governance Authority Chain
Research Stopping Rule
Five Whys with Stop Rule
Review Trigger Register
Timeboxed Inquiry Record

essence_one_line: Set a principled stopping point for chains of justification, explanation, authority, or delegation so action can proceed without hiding foundational assumptions.

compression_statement: When inquiry can continue indefinitely because every reason, cause, authority, or norm requires a prior one, Regress Termination Rule maps the chain, names the relevant link type, defines a context-appropriate stopping criterion, records accepted assumptions and uncertainty residue, and specifies when inquiry must reopen.

core_intuition: Some questions genuinely need deeper support, but unlimited demands for prior support can paralyze action or conceal unspoken foundations. The archetype works by making the base of reasoning explicit: not because the base is perfect, but because the decision needs a visible, contestable, and reviewable point from which to act.

canonical_formula: open_ended_support_chain + decision_need -> mapped_regress + explicit_base_or_sufficiency_rule + assumption_record + uncertainty_residue + reopening_condition

structural_problem: A decision, explanation, proof, policy, design, or action is blocked because each supporting reason calls for another prior reason, each authority requires a higher authority, each cause points to an earlier cause, or each value judgment demands a deeper justification. Without a termination rule, inquiry either never closes or closes invisibly by exhaustion, power, habit, or arbitrary deadline.

condition: Each answer generates another why, proof, or authority demand.

example: A product team cannot choose an onboarding assumption because every piece of user evidence is challenged as needing another study, another segmentation, or another causal proof.

condition: A decision depends on a premise that cannot be fully proven inside the current frame.

example: A policy must choose a fairness standard, but the legitimacy of every fairness standard can itself be debated.

condition: Root-cause analysis keeps moving to broader causes without an actionable level.

example: An incident review moves from operator error to training to incentives to culture to industry history without deciding which level will be intervened on now.

condition: Authority or approval keeps escalating recursively.

example: A compliance decision is sent from reviewer to manager to counsel to steering committee, but no rule says who has final authority for the risk class.

condition: The group alternates between premature closure and endless inquiry.

example: Some participants want to decide immediately from intuition, while others can always name another uncertainty that must be resolved first.

condition: The stakes require transparent assumptions rather than tacit defaults.

example: A safety case, public policy, grant decision, or model release must show what was accepted as a base and why that base is sufficient for the current action.

symptom: Repeated requests for more justification without changed decision criteria

diagnostic_meaning: The issue may be lack of a stopping criterion rather than lack of effort or intelligence.

symptom: Action delayed until every deeper cause or premise is resolved

diagnostic_meaning: The process is confusing useful due diligence with impossible final grounding.

symptom: Closure happens by fatigue, deadline, seniority, or silence

diagnostic_meaning: The regress is being terminated, but the termination is hidden and therefore difficult to audit or contest.

symptom: Different actors use different implicit standards for enough evidence

diagnostic_meaning: The process needs a shared sufficiency threshold calibrated to stakes and reversibility.

symptom: Old questions repeatedly reopen without a defined trigger

diagnostic_meaning: The group lacks a burden of reopening and review condition, so closure cannot stabilize.

symptom: Foundational assumptions drive decisions but appear nowhere in the decision record

diagnostic_meaning: The process may be pretending to be fully justified while relying on unrecorded premises.

root_tension: Responsible reasoning requires support, but action requires closure. If the chain closes too early, the decision may rest on prejudice, weak evidence, or arbitrary authority. If it never closes, the decision becomes impossible and practical authority shifts to delay, fatigue, or whoever can keep demanding more proof. The archetype preserves both accountability and action by making closure explicit and reopenable.

case: The main problem is a process, algorithm, workflow, or recursive operation that lacks general stop conditions.

case: The main problem is an iterative process that may or may not have stabilized enough to stop.

case: The main problem is deciding what is good enough under bounded rationality or opportunity cost.

case: The main problem is surfacing hidden assumptions without needing a closure rule for a live regress.

case: The main problem is choosing between values, stakeholders, or policy goals rather than terminating an endless chain of why those values count.

case: The activity is a philosophical discussion, debate, or teaching exercise with no decision, assumption record, or reopening governance.

structural_intervention: Transform an unbounded support demand into a bounded, transparent, and reviewable decision base. The intervention identifies the focal claim, maps the regress chain, distinguishes what kind of support is being requested, selects a stopping criterion suited to the stakes, documents accepted assumptions and uncertainty residue, and defines the conditions under which inquiry must reopen.

name: Name the claim or decision being held open

logic: Specify exactly what cannot move forward: a factual claim, causal explanation, policy choice, design assumption, approval, delegation, ethical standard, or proof.

Map the regress chain

logic: Write the sequence of support demands: this claim needs this reason, that reason needs this prior premise, that premise needs another authority, and so on.

Classify the link type

logic: Determine whether the chain is evidentiary, causal, definitional, normative, authority-based, procedural, feasibility-based, or mixed.

name: Identify candidate bases or sufficiency standards

logic: Find possible foundations: accepted axioms, legal authority, evidence thresholds, stakeholder ratification, first principles, bounded inquiry budgets, risk standards, or actionable causal levels.

Calibrate the threshold to stakes

logic: Set stricter requirements for irreversible, high-harm, public, contested, or high-uncertainty decisions and lighter requirements for reversible, low-stakes, exploratory decisions.

Declare the stopping criterion

logic: State the condition under which the chain is considered sufficiently grounded for the current decision scope.

Record assumptions and residue

logic: Document accepted premises, unresolved doubts, known weaknesses, dependencies, dissent, and why closure is still acceptable.

Define reopening conditions and burden

logic: Specify what evidence, failure, contradiction, changed stakes, or legitimate challenge reopens the chain, and who bears the burden for reopening.

Proceed within the declared scope

logic: Use the stopping point to enable action while preventing local closure from being treated as universal certainty.

rule: Use this archetype only when the support chain is potentially unbounded and is blocking action, legitimacy, or accountable closure.

rule: A stopping criterion is acceptable only if it is tied to the decision scope, stakes, reversibility, available evidence, and review conditions.

rule: Do not close by authority alone when the issue is evidentiary or normative; use link-type distinction to choose an appropriate base.

rule: The more irreversible or harmful the action, the stronger the sufficiency threshold and the more explicit the reopening conditions should be.

rule: If the same challenge can be raised after closure without any new evidence or changed stakes, define a burden of reopening rather than relitigating indefinitely.

rule: If new evidence undermines the accepted base, reopen the chain rather than defending closure as a matter of pride or process convenience.

description: Every closure point should identify the assumption, authority, evidence standard, or principle on which action rests.

description: The stopping rule closes the chain for a defined purpose, not for all possible purposes or all future contexts.

invariant: contestability

description: Affected or accountable actors should be able to see and challenge the basis for closure through a defined channel.

invariant: reopenability

description: Closure remains responsive to new evidence, changed stakes, contradiction, failure, or legitimacy challenges.

description: Remaining doubts are recorded rather than erased by the fact that action proceeds.

invariant: proportionality

description: The amount of justification demanded should be proportional to stakes, reversibility, urgency, and risk.

description: Decisions can proceed without waiting for impossible final grounding.

description: The premises and standards beneath a decision become visible and auditable.

description: Requests for more proof must connect to defined sufficiency or reopening rules rather than indefinite obstruction.

description: Inquiry effort is matched to stakes instead of defaulting to either impulsive closure or infinite review.

description: Stakeholders can inspect why the chain stopped, what remains uncertain, and how the decision can be revisited.

prose_essence:
Regress Termination Rule is the intervention pattern for deciding where a potentially endless chain of support should stop. It is useful when every answer invites another prior question: why accept this evidence, why trust that authority, why use that value, why stop at that cause, or why act before every assumption is proven.

The archetype does not pretend that foundations are absolute. It creates local, explicit, accountable closure. A team can say: for this decision, under these stakes, we will accept this assumption, authority, evidence standard, or principle as enough; here is what remains uncertain; and here is what would reopen the chain.

prose_when_to_use:
Use this archetype when a live decision or explanation is blocked by recursive demands for prior support. It fits governance decisions, policy rationales, research thresholds, incident analysis, strategy assumptions, formal modeling, and ethical or legal reasoning where complete justification is impossible inside the current frame.

It is especially useful when the group has confused responsible due diligence with infinite deferral. It is also useful when decisions are already being closed implicitly by fatigue, deadline, seniority, or institutional habit and those hidden foundations need to become visible.

Do not use it merely because a process needs a stop condition. A workflow timeout, algorithmic base case, convergence threshold, or project deadline may need Termination Condition Design or Convergence Criteria Design instead. This archetype applies when the chain being stopped is a chain of reasons, explanations, authorities, or norms.

prose_structural_problem:
The structural problem is an unbounded support chain. A claim depends on a reason; the reason depends on a prior premise; the premise depends on a further source; the source depends on a deeper authority, cause, definition, or value. The chain can continue indefinitely.

Without a termination rule, two bad outcomes become likely. The first is paralysis: no decision is ever justified enough to act. The second is hidden closure: action proceeds anyway, but the actual stopping point is power, fatigue, urgency, or an unstated assumption. In both cases, accountability suffers. Either no one can act, or no one can inspect why action was allowed.

The underlying tension is that responsible reasoning requires support, while practical action requires closure. Regress Termination Rule resolves this tension by making the stopping point explicit, scoped, and reopenable.

prose_intervention_logic:
The intervention begins by naming the focal claim or decision. This keeps the inquiry from expanding into every adjacent philosophical or operational question.

Next, the regress chain is mapped. The chain may involve evidentiary links, causal links, authority links, definitional links, feasibility links, or normative links. Distinguishing link types matters because each type requires a different base. Evidence questions need evidentiary sufficiency. Authority questions need legitimate jurisdiction. Normative questions need declared value sources or procedures. Causal questions need an actionable explanatory level.

The team then identifies candidate bases or sufficiency standards. These might be accepted axioms, first principles, burden-of-proof rules, evidence thresholds, legal authority, stakeholder ratification, risk standards, or bounded inquiry budgets. The threshold is calibrated to the stakes: irreversible and high-harm decisions demand stronger justification than reversible exploratory decisions.

Finally, the team records the stopping criterion, accepted assumptions, uncertainty residue, and reopening conditions. Closure is not treated as final truth. It is treated as a justified local base for action.

prose_key_components:
### Claim Under Justification

This component identifies the exact claim, decision, or action being held open. Without it, participants may keep changing the subject. The archetype works best when the group can name what needs justification now.

### Regress Chain

The regress chain is the visible sequence of support demands. It shows whether the group is asking for prior evidence, deeper cause, higher authority, clearer definition, stronger feasibility proof, or more fundamental value justification.

### Link Type Distinction

Link Type Distinction prevents the group from answering the wrong question. Authority cannot substitute for evidence unless the issue is legitimately procedural. Evidence cannot settle a value premise by itself. A causal explanation cannot automatically justify an ought-claim.

### Foundational Assumption

A foundational assumption is a premise accepted provisionally as a base for current action. It is not sacred. Its value is that it is visible, named, and contestable rather than hidden inside a decision.

### Stopping Criterion

The stopping criterion defines what counts as enough for the current purpose. It might be a legal standard, an evidence threshold, a risk threshold, a stakeholder approval condition, an accepted axiom, or a practical due-diligence rule.

### Sufficiency Threshold

The sufficiency threshold calibrates the strength of the stopping criterion. Reversible low-stakes decisions can use lighter thresholds. Irreversible, safety-critical, public, or rights-affecting decisions require stricter thresholds.

### Assumption Record

The assumption record documents the accepted base, why it was accepted, what depends on it, what remains weak, and who owns future review. It is the accountability artifact of the archetype.

### Reopening Condition

A reopening condition defines what would make the group revisit the closed chain. New evidence, changed stakes, an observed failure, affected-party challenge, legal change, or contradiction may all reopen the decision.

### Burden of Reopening

Burden of reopening defines who must provide what reason to challenge the closure after the decision has moved forward. It prevents both endless relitigation and rigid refusal to reconsider.

### Decision Scope Boundary

The decision scope boundary keeps local closure from becoming universal certainty. The rule should say what the closure is for: this decision, this time horizon, this risk class, this model, or this governance context.

### Uncertainty Residue

Uncertainty residue names the doubts that remain after closure. It lets action proceed without pretending that deeper questions have disappeared.

prose_common_mechanisms:
### First-Principles Statement

A first-principles statement names the base premises the group will reason from. It implements the archetype only when paired with scope, sufficiency, uncertainty, and reopening logic. Otherwise it is just a statement of belief.

### Assumption Log

An assumption log records accepted assumptions, dependencies, review owners, and reopening triggers. It is a mechanism for preserving accountability after the regress has been locally closed.

### Burden-of-Proof Rule

A burden-of-proof rule allocates responsibility for justification. It answers questions such as: who must prove that the claim is safe, fair, legal, feasible, or worth reopening?

### Decision Closure Criteria

Decision closure criteria turn abstract sufficiency into practical checks. They help a team decide whether enough support has been gathered for the decision class.

### Evidence Sufficiency Rubric

An evidence sufficiency rubric calibrates the quality, quantity, diversity, and relevance of evidence. It is especially useful when the regress is evidentiary rather than procedural or normative.

### Axiom Set

An axiom set is a formal base for reasoning. In mathematics, specification, modeling, or architecture, the axiom set can terminate proof regress by declaring what is accepted as primitive for the system.

### Governance Authority Chain

A governance authority chain defines who can close recursive approvals or appeals. It must be legitimate, scoped, and reviewable; otherwise it becomes simple power assertion.

### Research Stopping Rule

A research stopping rule defines when enough data collection, literature review, or analysis has been completed for a purpose. It is a mechanism, not the archetype itself, because it applies the pattern inside research.

### Five Whys with Stop Rule

Five Whys can expose deeper causes, but without a stop rule it can create endless explanatory regress. The mechanism implements the archetype only when it names the actionable level and review trigger.

### Review Trigger Register

A review trigger register preserves reopenability. It lists the events, evidence, or failures that would invalidate the current base or require renewed inquiry.

prose_failure_modes:
**Arbitrary closure** happens when the chain stops because people are tired, senior leaders demand action, or time runs out. The mitigation is a written stopping criterion and assumption record.

**Dogmatic foundation** happens when a provisional assumption becomes unquestionable. The mitigation is scope limitation and reopening triggers.

**Infinite deferral** happens when no sufficiency threshold is ever accepted. The mitigation is a burden-of-proof rule and proportional evidence standard.

**Wrong link type** happens when the group uses the wrong base for the question. Authority does not settle empirical evidence; data alone does not settle values. The mitigation is link-type distinction.

**Overbroad closure** happens when a local stopping rule is treated as global truth. The mitigation is a decision scope boundary.

**Hidden value foundation** happens when normative premises are disguised as neutral facts. Pair with Normative Assumption Explicitness when this risk appears.

prose_neighbor_distinctions:
**Termination Condition Design** stops processes, workflows, searches, or recursive operations. Regress Termination Rule stops chains of justification, explanation, authority, or norm support.

**Convergence Criteria Design** asks whether an iterative process has stabilized enough. Regress Termination Rule asks what foundation or sufficiency standard can end a support chain.

**Satisficing Threshold Design** defines good-enough action under bounded rationality. Regress Termination Rule may use satisficing logic, but only when the problem is recursive justification.

**Foundational Assumption Audit** would test the validity and consequences of existing assumptions. Regress Termination Rule sets and governs assumptions as stopping points for live decisions.

**Normative Assumption Explicitness** surfaces hidden ought-claims. Regress Termination Rule addresses the further problem that every ought-claim can invite a deeper why.

prose_cross_domain_examples:
In public policy, a team may accept a defined evidence threshold and public-value premise for a staged rollout while documenting what would trigger reversal or redesign.

In incident analysis, investigators may stop at the actionable control layer while recording deeper systemic contributors for separate governance review.

In product strategy, a team may proceed from a provisional market assumption after defined discovery work, then reopen if launch signals contradict the assumption.

In legal compliance, a process may define final internal authority for a classification and the evidence required to reopen it.

In formal modeling, a team may define accepted axioms and boundary assumptions before running proof or simulation work.

prose_non_examples:
A philosophical debate about whether foundations exist is not this archetype unless it produces an operational stopping rule for a decision.

A meeting ending because time ran out is not this archetype. The clock stopped, not the regress.

A manager refusing further questions is not this archetype. Suppression is not accountable closure.

A model stopping after numerical convergence is usually Convergence Criteria Design or Termination Condition Design, unless the issue is why that convergence is sufficient for action.

prose_tradeoffs:
The core tradeoff is closure versus truth-seeking. Stopping enables action, but it can truncate inquiry. The mitigation is proportional sufficiency plus reopening conditions.

Another tradeoff is stability versus responsiveness. If closure reopens too easily, action destabilizes. If it reopens too rarely, the foundation becomes dogma. Burden-of-reopening rules manage this tension.

There is also a transparency burden. Recording assumptions, support chains, and uncertainty takes effort. The level of documentation should scale with risk.

Finally, authority can produce clarity but also exclusion. If authority terminates a chain, its legitimacy and appeal boundaries must be explicit.

prose_invariants_to_preserve:
The first invariant is explicitness: the base assumption or sufficiency standard must be named.

The second invariant is scope limitation: the stopping rule closes the chain for a particular purpose, not for all possible purposes.

The third invariant is contestability: relevant actors must be able to inspect and challenge the rationale through a defined path.

The fourth invariant is reopenability: new evidence, changed stakes, or observed failure can reopen the chain.

The fifth invariant is uncertainty honesty: remaining doubts are recorded instead of erased.

prose_target_outcomes:
A good application produces actionable closure. The decision can move forward because the group knows what counts as enough.

It also produces transparent foundations. The premises, standards, authorities, and values beneath the decision are visible rather than tacit.

It reduces performative delay. Requests for more support must connect to declared criteria rather than functioning as indefinite obstruction.

It improves legitimacy because affected or accountable actors can see what was accepted, what remains uncertain, and how the decision may be revisited.

prose_parameter_dimensions:
The main tuning dimension is **depth of inquiry**: how far back the chain must be traced before closure is acceptable. High-stakes or poorly understood decisions need more depth.

Another dimension is **sufficiency strength**: how much support is enough. This depends on harm, reversibility, urgency, public accountability, legal duties, and confidence in evidence.

A third dimension is **scope of closure**. Closure can apply to a single experiment, one product release, a policy cycle, a legal classification, a model version, or a whole governance standard. Narrow closure is safer when uncertainty is high.

A fourth dimension is **reopenability**. Some decisions should reopen easily when new evidence appears; others need stability and require a stronger burden of reopening.

A fifth dimension is **authority distribution**. Closure can be made by an individual owner, a peer review group, a community process, a formal board, or a legal standard. The correct distribution depends on legitimacy and stakes.

prose_variants_and_near_names:
Justificatory Regress Termination applies to endless demands for reasons or evidence. Explanatory Regress Termination applies to endless causal or interpretive why-chains. Authority Regress Termination applies to recursive approval or appeal chains. Normative Regress Termination applies to values and ought-claims. Reopenable Foundational Closure emphasizes the review conditions that keep a base assumption accountable over time.

Near names include justification stop rule, explanation stop rule, regress closure rule, foundational assumption rule, first-principles statement, assumption log, burden-of-proof rule, research stopping rule, and termination condition. Most of these are mechanisms or components unless the full intervention pattern is present. `termination_condition` is recorded as a proposed prime in this draft because it appears prime-like in roadmap controls but is not canonical.

Justificatory Regress Termination

variant_type: subtype

short_definition: Stops an endless demand for reasons by defining what counts as enough justification for the present claim or decision.

distinctive_feature: The key object is justificatory support: reasons, evidence, warrants, definitions, or premises.

distinct_from_parent: It is narrower than the parent because it concerns reasons for accepting a claim, not causal, authority, procedural, or delegation regress in general.

still_same_parent_because: It uses the same structure: map the chain, identify a defensible base, document uncertainty, and define reopening conditions.

use_when: A person or group keeps asking why a claim should be accepted, and each answer triggers another demand for support.

use_when: A decision cannot proceed because the acceptable evidentiary or argumentative base is unclear.

domain: research

example: A review board states what level of evidence is sufficient to proceed with a pilot while documenting unresolved uncertainties.

domain: strategy

example: A leadership team accepts a market premise as provisional after defined due diligence rather than postponing every decision for more proof.

Explanatory Regress Termination

short_definition: Stops an endless chain of why or cause questions by defining the explanatory depth needed for the decision at hand.

distinctive_feature: The key object is explanation depth: how far back a causal or interpretive chain must go before action can be justified.

distinct_from_parent: It is narrower than the parent because it centers on explanation and cause rather than evidence, authority, or norm justification.

still_same_parent_because: It closes a potentially infinite chain through an explicit stopping criterion and review trigger.

use_when: Root-cause inquiry keeps moving to deeper causes without clarifying which level is actionable.

use_when: A causal explanation is needed, but complete ultimate explanation is impossible or irrelevant to intervention.

example: An investigation stops at a level where process controls can be redesigned, while noting deeper cultural factors for separate review.

domain: policy

example: A program evaluation identifies the causal pathway sufficient for a policy change without claiming a final explanation of all social conditions.

Authority Regress Termination

short_definition: Stops endless appeals to higher authorization by defining the legitimate decision authority or final escalation boundary.

distinctive_feature: The key object is legitimate authority: who may close the chain and under what mandate.

distinct_from_parent: It is narrower than the parent because the regress is institutional or procedural rather than mainly evidentiary or causal.

still_same_parent_because: The same intervention pattern applies: map the chain, identify the accepted base, document it, and define reopening or appeal conditions.

use_when: A decision keeps escalating because no one accepts that any actor has final authority.

use_when: Governance, compliance, or accountability structures create recursive approval demands.

domain: governance

example: A policy process specifies which committee can make the final call after consultation, instead of sending every objection to another body.

example: A launch decision defines one accountable owner and an appeal window rather than requiring approval by an unlimited chain of stakeholders.

Normative Regress Termination

short_definition: Stops endless requests for why a value, standard, or ought-claim matters by declaring the governing value source and contestation process.

distinctive_feature: The base is a value source, procedural standard, right, commitment, law, mission, or ethical principle rather than an empirical premise.

distinct_from_parent: It is narrower because the chain being terminated is about ought-claims and legitimacy rather than factual explanation.

still_same_parent_because: It uses explicit stopping and reopening governance to allow action without hiding value premises.

use_when: A decision depends on a value judgment, but each value is challenged by asking why that value should count.

use_when: A metric, policy, or rule embeds normative assumptions that must be explicit but cannot be justified indefinitely.

example: A model governance board states that equal contestability is a non-negotiable value for this release, rather than treating it as a hidden preference.

example: A city policy documents the public-value premise behind prioritizing accessibility, plus conditions under which the priority can be reconsidered.

Reopenable Foundational Closure

short_definition: Treats a foundational assumption as provisionally closed for action while defining precise review triggers.

distinctive_feature: The emphasis is on reopening governance rather than on the initial choice of stopping criterion.

distinct_from_parent: It is a narrower governance form of the parent that foregrounds how closure stays accountable over time.

still_same_parent_because: It still terminates a regress by naming a base, recording uncertainty, and specifying review conditions.

use_when: The group needs to act from a base assumption but expects new evidence, changed stakes, or dissent to emerge.

use_when: Closing the chain is necessary, but irreversible closure would be irresponsible.

example: A product team proceeds from a provisional user-risk assumption but defines the signal that will trigger redesign.

example: A safety case accepts a modeling assumption for certification while requiring review if incident patterns contradict it.

near_names:
Stopping Criterion
Foundational Assumption Setting
First-Principles Note
Assumption Log
Decision Closure
Burden-of-Proof Rule
Axiom Selection
Research Stopping Rule
Termination Condition


Regression-to-the-Mean Guardrail

type: archetype

source: generated

batch: Batch 19

aliases:
Regression-to-the-Mean Safeguard
Extreme-Selection Attribution Guardrail
Reversion Attribution Check
Statistical Regression Guardrail

slug: confounding

slug: overfitting

slug: probability

slug: uncertainty

slug: randomization

slug: counterfactuals

slug: calibration

slug: causality

slug: time

slug: variability

slug: bias

slug: observability

components: Signal Reliability and Noise Decomposition

components: Subgroup, Heterogeneity, and Tail-Case Review

components: Outcome Measurement and Observer Independence

components: Decision Hold, Release, and Reversal Guardrail

components: Replication, Monitoring, and Learning Loop

mechanisms: Placebo Time, Outcome, or Threshold Check

essence_one_line: Prevent ordinary reversion after extreme observations from being credited to an intervention, person, punishment, reward, or event without a credible counterfactual.

compression_statement: When cases are selected because an imperfect measurement is unusually high or low, estimate their expected no-effect movement, strengthen baseline and counterfactual evidence, decompose observed change, calibrate causal language, and constrain decisions until improvement or deterioration exceeds plausible regression to the mean.

core_intuition: Extreme observations often contain both persistent signal and transient noise. On remeasurement, the transient part is unlikely to recur in the same direction, so selected extremes tend to look less extreme even without causal change. The guardrail turns that statistical expectation into evaluation design and decision discipline.

canonical_formula: observed change = expected reversion + temporal process + measurement change + concurrent causes + intervention effect + unresolved uncertainty

structural_problem: Interventions, rewards, punishments, inspections, and stories often begin immediately after an extreme outcome. When the next observation is closer to the relevant average, the sequence feels causal even if part or all of the movement was predictable from selection and imperfect reliability.

trigger_conditions: eligibility follows a high or low threshold

trigger_conditions: worst or best performers are singled out

trigger_conditions: before-after change is the primary evidence

trigger_conditions: the measure has substantial transient variation

symptoms: no similarly selected untreated comparison exists

symptoms: claims disappear when less extreme cohorts are included

root_tension: Acting on extreme cases is often necessary, yet the same selection makes naïve before-after evidence especially vulnerable to false causal credit or blame.

anti_signatures: cases were not selected using the outcome or a correlated noisy measure

anti_signatures: outcomes are effectively deterministic and perfectly persistent

anti_signatures: a randomized concurrent contrast already identifies the effect with intact follow-up

anti_signatures: the task is only to standardize magnitude

anti_signatures: the question concerns general distribution-family fitness rather than post-selection attribution

structural_intervention: Embed an extreme-selection attribution guardrail that records the trigger, establishes the relevant expectation and reliability, strengthens baseline and counterfactual evidence, estimates no-effect reversion, decomposes observed movement, and constrains causal language and decisions until evidence exceeds plausible reversion.

action_logic: define the causal claim and decision consequence

action_logic: record whether selection depended on an extreme noisy observation

action_logic: define the relevant population, subgroup, time regime, and mean target

action_logic: estimate measurement reliability and transient variation

action_logic: collect repeated pre-intervention baselines where feasible

action_logic: map trend, seasonality, maturation, recovery, and concurrent events

action_logic: construct a similarly selected concurrent counterfactual

action_logic: preserve assignment and exposure integrity

action_logic: estimate a no-effect reversion benchmark

action_logic: decompose observed movement and report residual uncertainty

action_logic: report controlled effect magnitude rather than raw change

action_logic: review subgroup and persistent-tail cases

action_logic: hold or limit consequential decisions when design is weak

decision_rules: the more extreme the selection and the less reliable the measure, the larger the expected reversion concern

decision_rules: do not infer causal effect from selected-case before-after change alone

decision_rules: comparators should face the same selection rule, measurement schedule, and temporal environment

decision_rules: do not mechanically shrink a case toward an irrelevant pooled mean

decision_rules: if credible reversion accounts for the observed movement, label effect unresolved unless a stronger contrast exists

decision_rules: if high-stakes action is irreversible, require stronger counterfactual and persistence evidence

decision_rules: if treatment cannot ethically be withheld, use repeated baseline, staggered timing, matched extremes, series evidence, and sensitivity together

decision_rules: regression-to-mean awareness is not proof that an intervention has no effect

target_invariants: reference mean is population- and time-relevant

target_invariants: reliability and measurement change remain visible

target_invariants: raw change remains distinct from controlled effect

target_invariants: persistent extremes and vulnerable cases are not dismissed

target_invariants: decision safeguards respond to evidence quality

expected_outcomes: fairer reward, sanction, and personnel decisions

expected_outcomes: less overreaction to threshold recrossing

expected_outcomes: better protection of genuine persistent extremes

expected_outcomes: clearer uncertainty and counterfactual reasoning

prose_essence:
Regression-to-the-Mean Guardrail prevents ordinary reversion after extreme observations from being mistaken for causal effect, stable personal change, managerial success, punishment effectiveness, or treatment failure. It is a design and decision discipline, not a slogan applied after results are known.

When cases are selected because a noisy measurement is unusually high or low, that observation usually contains both persistent signal and transient deviation. On remeasurement, the transient part is unlikely to recur in the same direction. The selected group therefore tends to look less extreme even if nothing causal happens.

The guardrail does not assume every change is spurious. It estimates expected no-effect movement, strengthens temporal and concurrent comparisons, preserves measurement integrity, reports controlled effect and uncertainty, and constrains consequential attribution until evidence exceeds plausible reversion.

prose_when_to_use:
Use it whenever treatment, inspection, coaching, reward, sanction, funding, investigation, or follow-up begins because an outcome crossed a threshold, entered the top or bottom of a ranking, reached crisis severity, or otherwise looked exceptional. Use it when the next observation will be compared with that extreme baseline.

The risk grows as selection becomes more extreme, measurement becomes less reliable, and sample size becomes smaller. It is also important when the reference population changes, outcomes trend or cycle, follow-up is selective, or the decision affects rights, safety, access, reputation, or livelihood.

Do not turn the guardrail into fatalism. Genuine treatment effects, persistent extremes, structural harm, and durable improvement exist. The objective is to demand an appropriate counterfactual and calibrated claim, not to insist that every observed change is random.

prose_structural_problem:
Human attention and institutional action are triggered by extremes. A patient seeks care when symptoms peak; a school is targeted after a bad year; a manager praises a record month or sanctions a disastrous one; an inspector intervenes after a defect spike. The timing creates a compelling before-intervention/after-intervention story.

That story is structurally biased because the selection observation is not an ordinary baseline. Without repeated baseline data, reliability evidence, a similarly selected comparison, and temporal context, the expected no-effect path is already toward a less extreme observation. Raw change therefore combines reversion with any genuine effect.

The central tension is urgent response versus credible learning. Extreme cases often need action precisely when clean experimentation is hardest. The archetype supports action while separating service or safety decisions from overconfident attribution and preserving evidence for later learning.

prose_intervention_logic:
1. **Guardrail step 1.** Define the causal claim and decision consequence. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
2. **Guardrail step 2.** Record whether selection depended on an extreme noisy observation. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
3. **Guardrail step 3.** Define the relevant population, subgroup, time regime, and mean target. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
4. **Guardrail step 4.** Estimate measurement reliability and transient variation. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
5. **Guardrail step 5.** Collect repeated pre-intervention baselines where feasible. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
6. **Guardrail step 6.** Map trend, seasonality, maturation, recovery, and concurrent events. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
7. **Guardrail step 7.** Construct a similarly selected concurrent counterfactual. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
8. **Guardrail step 8.** Preserve assignment and exposure integrity. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
9. **Guardrail step 9.** Estimate a no-effect reversion benchmark. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
10. **Guardrail step 10.** Decompose observed movement and report residual uncertainty. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
11. **Guardrail step 11.** Report controlled effect magnitude rather than raw change. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
12. **Guardrail step 12.** Review subgroup and persistent-tail cases. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
13. **Guardrail step 13.** Stabilize outcome measurement. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
14. **Guardrail step 14.** Grade attribution language. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
15. **Guardrail step 15.** Hold or limit consequential decisions when design is weak. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.
16. **Guardrail step 16.** Replicate across time and cohorts. Record the owner, selection timing, evidence, uncertainty, and the downstream claim or decision constrained by the result.

The lifecycle begins before outcome review when possible. Once an extreme baseline is celebrated or condemned, later analysis is vulnerable to selective comparison. Pre-specifying selection, outcomes, comparators, and claim thresholds preserves both learning and fairness.

prose_key_components:
### Causal Claim and Decision Scope (`causal_claim_and_decision_scope`)

Defines the claimed improvement or deterioration, the intervention or event credited, the affected decision, and the consequence of a false attribution.

Semantic canonical mapping retained the complete legacy component record: {"slug":"causal_claim_and_decision_scope","name":"Causal Claim and Decision Scope","role":"Defines the claimed improvement or deterioration, the intervention or event credited, the affected decision, and the consequence of a false attribution.","notes":"The guardrail is strongest when it starts before results are seen. It distinguishes descriptive change from a causal claim and identifies which promotion, treatment, sanction, funding, or policy decision could be distorted.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Extreme Selection and Trigger Rule (`extreme_selection_and_trigger_rule`)

Documents whether cases entered attention, treatment, evaluation, or follow-up because an initial measurement was unusually high, low, bad, good, or threshold-crossing.

Semantic canonical mapping retained the complete legacy component record: {"slug":"extreme_selection_and_trigger_rule","name":"Extreme Selection and Trigger Rule","role":"Documents whether cases entered attention, treatment, evaluation, or follow-up because an initial measurement was unusually high, low, bad, good, or threshold-crossing.","notes":"Regression to the mean is especially relevant when selection is conditioned on an extreme noisy observation. The trigger must include thresholds, ranking windows, retries, discretion, and who was not selected.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Reference Population and Mean Target (`reference_population_and_mean_target`)

Defines the population, process, subgroup, time regime, and reference expectation toward which repeated observations may revert.

Semantic canonical mapping retained the complete legacy component record: {"slug":"reference_population_and_mean_target","name":"Reference Population and Mean Target","role":"Defines the population, process, subgroup, time regime, and reference expectation toward which repeated observations may revert.","notes":"There is no meaningful regression-to-mean claim without a stable comparison target. Population drift, subgroup mixing, seasonality, or changing measurement can move the relevant mean.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Signal Reliability and Noise Decomposition (`signal_reliability_and_noise_decomposition`)

Separates persistent case differences from transient variation, measurement error, random shocks, and context-specific noise.

Semantic canonical mapping retained the complete legacy component record: {"slug":"signal_reliability_and_noise_decomposition","name":"Signal Reliability and Noise Decomposition","role":"Separates persistent case differences from transient variation, measurement error, random shocks, and context-specific noise.","notes":"Greater unreliability and more extreme selection imply stronger expected reversion. Reliability evidence should be local to the measure, interval, and population rather than imported from an unrelated setting.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Pre-Intervention Repeated Baseline (`pre_intervention_repeated_baseline`)

Uses multiple observations before the intervention or focal event to estimate the case's typical level and determine whether the selection observation was exceptional.

Semantic canonical mapping retained the complete legacy component record: {"slug":"pre_intervention_repeated_baseline","name":"Pre-Intervention Repeated Baseline","role":"Uses multiple observations before the intervention or focal event to estimate the case's typical level and determine whether the selection observation was exceptional.","notes":"One baseline cannot reveal whether an extreme value is persistent, trending, seasonal, or transient. Repeated baselines reduce dependence on a lucky or unlucky selection moment.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Time-Process and Regime Map (`time_process_and_regime_map`)

Maps trend, seasonality, maturation, decay, recurrence, recovery cycles, policy timing, and other temporal processes that can resemble or mask ordinary reversion.

Semantic canonical mapping retained the complete legacy component record: {"slug":"time_process_and_regime_map","name":"Time-Process and Regime Map","role":"Maps trend, seasonality, maturation, decay, recurrence, recovery cycles, policy timing, and other temporal processes that can resemble or mask ordinary reversion.","notes":"Regression to the mean is not a universal pull toward a timeless average. Temporal structure can change the expected counterfactual and must be separated from reversion.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Concurrent Counterfactual Comparison (`concurrent_counterfactual_comparison`)

Constructs a credible untreated, unexposed, not-yet-treated, or otherwise comparable path subject to the same selection and measurement environment.

Semantic canonical mapping retained the complete legacy component record: {"slug":"concurrent_counterfactual_comparison","name":"Concurrent Counterfactual Comparison","role":"Constructs a credible untreated, unexposed, not-yet-treated, or otherwise comparable path subject to the same selection and measurement environment.","notes":"If both selected treated and comparable untreated cases improve, the shared movement is evidence against attributing all change to treatment. Comparators must mirror selection and observation, not merely resemble average cases.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Assignment and Exposure Integrity (`assignment_and_exposure_integrity`)

Records randomization, staggered timing, eligibility, crossover, adherence, co-interventions, and contamination that determine whether outcome differences support attribution.

Semantic canonical mapping retained the complete legacy component record: {"slug":"assignment_and_exposure_integrity","name":"Assignment and Exposure Integrity","role":"Records randomization, staggered timing, eligibility, crossover, adherence, co-interventions, and contamination that determine whether outcome differences support attribution.","notes":"Random or plausibly exogenous assignment can separate intervention effect from expected reversion, but only when exposure, follow-up, and analysis preserve the assignment contrast.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Expected Reversion Benchmark (`expected_reversion_benchmark`)

Estimates how much movement would be expected from extreme selection and imperfect reliability even if no causal intervention occurred.

Semantic canonical mapping retained the complete legacy component record: {"slug":"expected_reversion_benchmark","name":"Expected Reversion Benchmark","role":"Estimates how much movement would be expected from extreme selection and imperfect reliability even if no causal intervention occurred.","notes":"The benchmark may use repeated historical episodes, untreated extremes, simulation, shrinkage, or reliability-based expectations. It should be a range, not a rhetorical label.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Observed Change Decomposition (`observed_change_decomposition`)

Partitions observed movement among expected reversion, secular trend, maturation, seasonality, measurement change, concurrent events, intervention effect, and unresolved uncertainty.

Semantic canonical mapping retained the complete legacy component record: {"slug":"observed_change_decomposition","name":"Observed Change Decomposition","role":"Partitions observed movement among expected reversion, secular trend, maturation, seasonality, measurement change, concurrent events, intervention effect, and unresolved uncertainty.","notes":"The decomposition prevents the residual from being treated as known merely because named alternatives were considered. Some shares may remain unidentified and should stay explicit.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Effect Size and Uncertainty Contract (`effect_size_and_uncertainty_contract`)

Reports the intervention contrast, uncertainty, practical meaning, denominator, and sensitivity to selection and baseline choices rather than celebrating raw before-after movement.

Semantic canonical mapping retained the complete legacy component record: {"slug":"effect_size_and_uncertainty_contract","name":"Effect Size and Uncertainty Contract","role":"Reports the intervention contrast, uncertainty, practical meaning, denominator, and sensitivity to selection and baseline choices rather than celebrating raw before-after movement.","notes":"A large raw change among extreme cases can coexist with a small or uncertain causal effect. The contract keeps magnitude, precision, and decision relevance separate.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Subgroup, Heterogeneity, and Tail-Case Review (`subgroup_heterogeneity_and_tail_case_review`)

Checks whether expected reversion, reliability, treatment effect, or decision harm differs across subgroups, severity levels, sites, cohorts, or rare cases.

Semantic canonical mapping retained the complete legacy component record: {"slug":"subgroup_heterogeneity_and_tail_case_review","name":"Subgroup, Heterogeneity, and Tail-Case Review","role":"Checks whether expected reversion, reliability, treatment effect, or decision harm differs across subgroups, severity levels, sites, cohorts, or rare cases.","notes":"Pooled correction can over-shrink genuine persistent extremes or erase groups with different baselines. Guardrails must not turn regression-to-mean awareness into automatic disbelief of severe cases.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Outcome Measurement and Observer Independence (`outcome_measurement_and_observer_independence`)

Protects follow-up measurement from changed instruments, awareness, incentives, differential scrutiny, and selective outcome choice after an extreme trigger.

Semantic canonical mapping retained the complete legacy component record: {"slug":"outcome_measurement_and_observer_independence","name":"Outcome Measurement and Observer Independence","role":"Protects follow-up measurement from changed instruments, awareness, incentives, differential scrutiny, and selective outcome choice after an extreme trigger.","notes":"If measurement changes after selection, apparent reversion may reflect instrument or observer behavior. Stable definitions and independent measurement preserve the comparison.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Attribution Language and Evidence Grade (`attribution_language_and_evidence_grade`)

Calibrates claims from observed change through association to credible causal effect according to design strength and unresolved regression risk.

Semantic canonical mapping retained the complete legacy component record: {"slug":"attribution_language_and_evidence_grade","name":"Attribution Language and Evidence Grade","role":"Calibrates claims from observed change through association to credible causal effect according to design strength and unresolved regression risk.","notes":"Communication should name selection, expected reversion, comparator evidence, and uncertainty. It should avoid both triumphal causal language and blanket dismissal of real improvement.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Decision Hold, Release, and Reversal Guardrail (`decision_hold_release_and_reversal_guardrail`)

Sets rules for delaying, limiting, reversing, or independently reviewing decisions when raw change is likely to be dominated by reversion or when evidence cannot separate causes.

Semantic canonical mapping retained the complete legacy component record: {"slug":"decision_hold_release_and_reversal_guardrail","name":"Decision Hold, Release, and Reversal Guardrail","role":"Sets rules for delaying, limiting, reversing, or independently reviewing decisions when raw change is likely to be dominated by reversion or when evidence cannot separate causes.","notes":"The guardrail gives analysis operational force. High-stakes reward, punishment, treatment, or continuation decisions should not rest on a single extreme-before/ordinary-after sequence.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

### Replication, Monitoring, and Learning Loop (`replication_monitoring_and_learning_loop`)

Tracks whether effects persist across later measurements, new cohorts, less extreme entrants, and repeated implementation while updating the expected-reversion benchmark.

Semantic canonical mapping retained the complete legacy component record: {"slug":"replication_monitoring_and_learning_loop","name":"Replication, Monitoring, and Learning Loop","role":"Tracks whether effects persist across later measurements, new cohorts, less extreme entrants, and repeated implementation while updating the expected-reversion benchmark.","notes":"True effects should demonstrate durability or reproducible contrasts appropriate to the process. Monitoring also detects when reliability, population, or selection rules change.","component_type":"required_structural_component","reuse_scope":"cross_domain","maturity":"provisional","host_archetypes":["regression_to_mean_guardrail"],"evidence_expectation":"Record selection timing, comparison logic, uncertainty, owner, affected decision, and evidence that distinguishes reversion from intervention or stable change.","not_a_mechanism_because":"This is a persistent attribution responsibility, not a particular control group, repeated measure, model, plot, simulation, or statistical test."}

prose_common_mechanisms:
### Extreme-Selection Risk Flag (`extreme_selection_risk_flag`)

**Type:** `screening`

Marks evaluations triggered by threshold crossing, top/bottom ranking, crisis entry, exceptional performance, or repeated testing after an extreme result.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"extreme_selection_risk_flag","name":"Extreme-Selection Risk Flag","mechanism_type":"screening","role":"Marks evaluations triggered by threshold crossing, top/bottom ranking, crisis entry, exceptional performance, or repeated testing after an extreme result.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Multi-Baseline Measurement Protocol (`multi_baseline_measurement_protocol`)

**Type:** `measurement`

Collects repeated pre-intervention observations to estimate typical level, reliability, trend, and the extremeness of the selection observation.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"multi_baseline_measurement_protocol","name":"Multi-Baseline Measurement Protocol","mechanism_type":"measurement","role":"Collects repeated pre-intervention observations to estimate typical level, reliability, trend, and the extremeness of the selection observation.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Matched Extreme-Case Comparator (`matched_extreme_case_comparator`)

**Type:** `comparison`

Compares treated extreme cases with untreated or not-yet-treated cases selected by the same threshold and observed on the same schedule.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"matched_extreme_case_comparator","name":"Matched Extreme-Case Comparator","mechanism_type":"comparison","role":"Compares treated extreme cases with untreated or not-yet-treated cases selected by the same threshold and observed on the same schedule.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Randomized or Staggered Assignment (`randomized_or_staggered_assignment`)

**Type:** `experimental_design`

Creates an intervention contrast that is less confounded by expected reversion after extreme eligibility.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"randomized_or_staggered_assignment","name":"Randomized or Staggered Assignment","mechanism_type":"experimental_design","role":"Creates an intervention contrast that is less confounded by expected reversion after extreme eligibility.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Reliability-Based Reversion Simulation (`reliability_based_reversion_simulation`)

**Type:** `simulation`

Uses plausible reliability, variance, and selection thresholds to estimate the no-effect distribution of follow-up movement.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"reliability_based_reversion_simulation","name":"Reliability-Based Reversion Simulation","mechanism_type":"simulation","role":"Uses plausible reliability, variance, and selection thresholds to estimate the no-effect distribution of follow-up movement.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Shrinkage-Aware Expectation (`shrinkage_aware_expectation`)

**Type:** `estimation`

Combines noisy case evidence with a relevant group expectation so extreme predictions are not treated as perfectly persistent.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"shrinkage_aware_expectation","name":"Shrinkage-Aware Expectation","mechanism_type":"estimation","role":"Combines noisy case evidence with a relevant group expectation so extreme predictions are not treated as perfectly persistent.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Controlled Before–After Contrast (`controlled_before_after_contrast`)

**Type:** `evaluation`

Compares change over the same interval between selected treated and credible comparison paths.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"controlled_before_after_contrast","name":"Controlled Before–After Contrast","mechanism_type":"evaluation","role":"Compares change over the same interval between selected treated and credible comparison paths.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Interrupted Series with Pretrend Check (`interrupted_series_with_pretrend_check`)

**Type:** `time_series`

Uses multiple pre- and post-event observations to separate a level or slope change from trend, seasonality, and transient extremes.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"interrupted_series_with_pretrend_check","name":"Interrupted Series with Pretrend Check","mechanism_type":"time_series","role":"Uses multiple pre- and post-event observations to separate a level or slope change from trend, seasonality, and transient extremes.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Placebo Time, Outcome, or Threshold Check (`placebo_time_outcome_or_threshold_check`)

**Type:** `falsification`

Tests whether apparent effects also appear where the intervention should not operate or at alternative selection moments.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"placebo_time_outcome_or_threshold_check","name":"Placebo Time, Outcome, or Threshold Check","mechanism_type":"falsification","role":"Tests whether apparent effects also appear where the intervention should not operate or at alternative selection moments.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

### Attribution-Claim Review Gate (`attribution_claim_review_gate`)

**Type:** `governance`

Requires selection, counterfactual, expected-reversion, uncertainty, subgroup, and persistence evidence before approving a causal success or failure claim.

Selection constraints and retained implementation evidence: mirror the extreme selection process; preserve timing and measurement comparability; report uncertainty and unresolved causal alternatives; Complete legacy mechanism record retained: {"slug":"attribution_claim_review_gate","name":"Attribution-Claim Review Gate","mechanism_type":"governance","role":"Requires selection, counterfactual, expected-reversion, uncertainty, subgroup, and persistence evidence before approving a causal success or failure claim.","maturity":"provisional","instantiates_archetypes":["regression_to_mean_guardrail"],"selection_constraints":["mirror the extreme selection process","preserve timing and measurement comparability","report uncertainty and unresolved causal alternatives"],"not_an_archetype_because":"This is a concrete flag, measurement protocol, comparison, assignment design, simulation, estimator, series analysis, falsification check, or review gate within the broader attribution safeguard."}

prose_failure_modes:
### Extreme Selection Omitted

**Cause.** The analysis never records that intervention or attention began because the baseline was unusually high or low.

**Mitigation.** Make the trigger rule and unselected cases explicit before outcome review. Recheck the attribution grade and the downstream decision after remediation.

### Single Baseline Attribution

**Cause.** One noisy extreme observation defines the baseline and all later movement is credited to treatment or management.

**Mitigation.** Use repeated pre-intervention measures and a reliability estimate. Recheck the attribution grade and the downstream decision after remediation.

### Average Comparator Mismatch

**Cause.** Selected extreme cases are compared with ordinary average cases rather than similarly selected untreated cases.

**Mitigation.** Mirror selection threshold, timing, opportunity, and measurement in the comparator. Recheck the attribution grade and the downstream decision after remediation.

### Timeless Mean Fallacy

**Cause.** Reversion is asserted toward a historical mean despite trend, seasonality, maturation, or regime change.

**Mitigation.** Define the relevant population-time expectation and map temporal structure. Recheck the attribution grade and the downstream decision after remediation.

### Raw Change Equals Effect

**Cause.** Before-after movement is reported as causal effect without expected-reversion or counterfactual decomposition.

**Mitigation.** Report the controlled contrast, expected reversion range, and unresolved alternatives. Recheck the attribution grade and the downstream decision after remediation.

### Overcorrection To Null

**Cause.** Regression-to-mean awareness becomes a reason to dismiss every improvement, decline, or persistent extreme.

**Mitigation.** Use design evidence and persistence; do not treat the guardrail as proof of no effect. Recheck the attribution grade and the downstream decision after remediation.

### Unfair Shrinkage

**Cause.** Individuals or subgroups with real persistent extremes are pulled mechanically toward an inappropriate pooled mean.

**Mitigation.** Use relevant subgroup expectations, heterogeneity review, and protected tail-case escalation. Recheck the attribution grade and the downstream decision after remediation.

### Measurement Change After Trigger

**Cause.** Follow-up uses different instruments, observers, incentives, or scrutiny than the selection measurement.

**Mitigation.** Stabilize outcome definitions and audit observer and instrument independence. Recheck the attribution grade and the downstream decision after remediation.

### Selective Followup And Survivorship

**Cause.** Only cases with convenient follow-up or continued exposure remain in the apparent success sample.

**Mitigation.** Preserve cohort flow, missing outcomes, exposure, and intention-based contrasts. Recheck the attribution grade and the downstream decision after remediation.

### Counterfactual Contamination

**Cause.** Comparison units receive the intervention, face different concurrent events, or are observed on different schedules.

**Mitigation.** Track assignment, exposure, spillover, co-intervention, and measurement parity. Recheck the attribution grade and the downstream decision after remediation.

### Claim Language Outpaces Design

**Cause.** A weak before-after pattern is communicated as proof, cure, accountability, or ability.

**Mitigation.** Grade attribution language to design strength and disclose selection and reversion risk. Recheck the attribution grade and the downstream decision after remediation.

### One Cycle Learning

**Cause.** A single cohort's rebound becomes permanent policy without replication across later, less extreme, or differently timed cases.

**Mitigation.** Require persistence, repeated cohorts, and updated reversion benchmarks. Recheck the attribution grade and the downstream decision after remediation.

prose_neighbor_distinctions:
### `effect_size_standardization`

Standardizes magnitude for interpretation. It does not by itself distinguish causal effect from ordinary reversion after extreme selection.

### `effort_based_vs_inherent_ability_attribution`

Calibrates performance explanations among effort, ability, strategy, and luck. Regression-to-the-Mean Guardrail is a general evaluation structure for any extreme-selected intervention or event.

### `heuristic_calibration_and_confidence_judgment`

Improves judgment confidence and heuristic use. This archetype focuses specifically on repeated outcomes after extreme selection and the risk of causal misattribution.

### `knowledge_threshold_crossing_communication`

Communicates when evidence crosses a knowledge threshold. The guardrail determines whether apparent post-selection change supplies valid causal evidence in the first place.

### `time_series_cross_section_analysis`

Combines temporal and cross-sectional comparisons as an analysis design. It can implement the guardrail but does not own its selection, reversion, attribution, and decision safeguards.

### `trend_detection_and_removal`

Separates systematic trend from residual movement. Trend is one competing temporal explanation; regression to the mean arises from extreme selection plus imperfect persistence.

### `counterfactual_comparison`

Compares actual and plausible alternative paths broadly. This guardrail is a distinct frozen neighbor specialized to extreme-selection reversion and its attribution hazards.

### `confounder_control`

Controls common causes of exposure and outcome. Regression to the mean can occur without a third-variable confounder and requires explicit selection and reliability evidence.

### `selection_bias_mitigation`

Addresses nonrepresentative inclusion and conditioning generally. This archetype focuses on movement expected when the inclusion variable is itself an extreme noisy measure.

### `effect_size_reporting`

Reports magnitude and practical meaning. A well-reported raw before-after effect can still be mostly expected reversion.

### `stationarity_validation`

Checks whether past predictive conditions persist. The guardrail uses a relevant mean and temporal map but owns extreme-selection causal attribution rather than general regime stability.

### `distributional_assumption_governance`

Governs probability-family commitments. It may support a reversion benchmark, while this q46 archetype governs design and attribution after extreme selection.

Frozen reconciliation provides the strongest boundary evidence: Counterfactual Comparison is a broad causal/value comparison archetype and explicitly lists `regression_to_mean_guardrail` as a neighbor risk. The guardrail survives because extreme-selection reversion has its own triggers, evidence requirements, misuse risks, and decision protections.

prose_cross_domain_examples:
### Clinical Care

Estimate expected symptom reversion after crisis-triggered enrollment before crediting all improvement to treatment.

Severity selection, natural fluctuation, recovery, and high-stakes continuation decisions coincide.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Education

Compare lowest-scoring schools with similarly selected not-yet-treated schools before crediting intervention for score rebound.

Noisy rankings and threshold targeting create predictable convergence.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Workforce And Sales

Avoid crediting a warning for an unusually poor month or praise for an unusually strong month without repeated performance evidence.

Reward and sanction follow extreme metrics with substantial transient noise.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Public Safety

Evaluate hotspot intervention against matched extreme areas and pretrends rather than raw decline after a spike.

Sites are selected after exceptional incident counts and face common temporal forces.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Quality Management

Distinguish natural return after an extreme defect period from durable process improvement.

Inspection and corrective action are triggered by control-limit breaches.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Risk Screening

Require stable retest and outcome evidence before treating threshold recrossing as risk elimination.

Measurement error and threshold selection can produce apparent normalization.
 The guardrail documents selection, reliability, relevant expectation, counterfactual, expected reversion, controlled effect, claim grade, and decision response.

### Extended school-improvement example

A school district directs support to the ten schools with the largest one-year score decline. A naïve evaluation would compare the unusually low selection year with the next year and credit all rebound to support. The guardrail records the ranking and threshold, reconstructs multiple prior years, estimates score reliability, maps cohort and policy changes, and selects comparison schools using the same extreme-decline rule in later rollout waves. Analysts estimate expected no-effect rebound from historical extreme episodes and compare controlled changes, subgroup outcomes, attendance, and independent measures. The result shows substantial ordinary reversion plus a smaller uncertain intervention contrast. Leaders continue low-risk support, avoid punitive claims about schools whose scores remain volatile, pre-register the next cohort, and require persistence before scaling expensive elements.

Across domains, the intervention is stable: reveal selection on an extreme, estimate ordinary reversion, strengthen comparison, constrain attribution, and preserve action proportional to evidence.

prose_non_examples:
- Calling every post-crisis improvement a treatment cure
- Calling every improvement mere regression without comparison
- Comparing worst performers with average performers
- Reporting raw before-after change as controlled effect
- Mechanically shrinking every rare or severe case to a pooled mean
- Using a single reversion correction with no decision safeguard

A warning about regression to the mean is not sufficient. The archetype requires a relevant mean, reliability, comparison, uncertainty, attribution grade, and enforceable decision safeguard.

prose_tradeoffs:
- **Rapid action versus stronger baseline evidence:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Ethical treatment access versus untreated comparison:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Simple explanation versus causal uncertainty:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Individual evidence versus pooled shrinkage:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Local comparator fit versus sample size:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Repeated measurement versus burden:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Conservative claims versus learning speed:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Randomization strength versus operational feasibility:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Follow-up duration versus decision latency:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Stable outcome definitions versus evolving practice:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **False positive effect claims versus false dismissal of real change:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.
- **Standardized guardrails versus domain context:** resolve using outcome reliability, selection extremity, causal stakes, ethical constraints, decision reversibility, and who bears false attribution.

Evaluation rigor has cost, but poor attribution also has cost: ineffective programs are scaled, useful ones are abandoned, people are rewarded or punished for noise, severe cases are disbelieved, and future learning is trained on false stories.

prose_invariants_to_preserve:
- Selection rule remains explicit.
- Reference mean is population- and time-relevant.
- Reliability and measurement change remain visible.
- Comparison mirrors extreme selection.
- Raw change remains distinct from controlled effect.
- Unresolved causes remain unresolved.
- Persistent extremes and vulnerable cases are not dismissed.
- Causal language matches design strength.
- Decision safeguards respond to evidence quality.
- Learning extends beyond one cohort.
- Regression to the mean remains a probabilistic expectation, not a deterministic law for each case.
- A weak causal design never becomes strong merely by applying a statistical correction.
- Urgent service and safety obligations remain distinct from claims that the intervention caused improvement.

These invariants allow mechanisms to vary while protecting attribution, dignity, and learning. They prevent both causal exaggeration and misuse of statistical caution to dismiss real effects or persistent need.

prose_target_outcomes:
- Fewer false success and failure claims.
- Better treatment and program evaluation.
- Fairer reward, sanction, and personnel decisions.
- More credible effect estimates.
- Less overreaction to threshold recrossing.
- Better protection of genuine persistent extremes.
- Clearer uncertainty and counterfactual reasoning.
- More reproducible causal learning.

Success means decisions and claims reflect what the evidence can distinguish. The system may still act under uncertainty, but it no longer converts an extreme-before/ordinary-after sequence into automatic proof.

prose_parameter_dimensions:
- **Selection extremity:** Distance from the relevant expectation and number of selection opportunities.
- **Measurement reliability:** Persistent signal relative to transient noise in the actual population and interval.
- **Baseline depth:** Number, spacing, and comparability of pre-intervention observations.
- **Reference mean:** Population, subgroup, cohort, site, and time regime used for expected reversion.
- **Comparator fidelity:** Similarity in selection rule, timing, measurement, opportunity, and concurrent conditions.
- **Assignment strength:** Random, staggered, quasi-random, matched, or observational exposure contrast.
- **Temporal complexity:** Trend, seasonality, maturation, recurrence, recovery, and regime changes.
- **Reversion benchmark width:** Uncertainty in the expected no-effect movement.
- **Effect threshold:** Controlled magnitude needed for practical or causal claims.
- **Follow-up horizon:** Time needed to distinguish transient rebound from durable change.
- **Subgroup granularity:** Extent to which reliability, baseline, and effect differ across groups.
- **Measurement independence:** Protection against changed instruments, observers, incentives, and scrutiny.
- **Decision reversibility:** Ability to delay, limit, reverse, or monitor action while evidence develops.
- **Claim grade:** Observed change, association, suggestive effect, credible effect, or replicated effect.
- **Replication breadth:** New cohorts, less extreme entrants, sites, and time periods.
- **Ethical comparison constraint:** Limits on withholding, delay, randomization, or data use.

Tune jointly. More extreme selection raises reversion concern; higher reliability reduces it. Better comparators strengthen attribution but may be unavailable or unethical. Longer follow-up improves durability evidence but delays decisions. In urgent contexts, act for safety while limiting the causal claim.

prose_variants_and_near_names:
### Clinical and Service Recovery Guardrail

Guard treatment or service claims when people enter care at unusually severe moments and may improve partly through ordinary fluctuation or recovery.

Centers severity-triggered entry, symptom course, repeated baselines, untreated comparators, clinically meaningful effect, and non-abandonment safeguards. Extreme selection, expected reversion, counterfactual comparison, calibrated attribution, and persistence remain shared.

Use it when entry follows symptom or performance crisis; and outcomes fluctuate naturally; and before-after improvement drives continuation or funding. Characteristic failures include natural recovery credited entirely to care, genuine severe case dismissed as noise, selective follow-up, harmful treatment retained.
### Performance Reward and Sanction Guardrail

Guard reward, punishment, promotion, dismissal, coaching, or accountability claims triggered by unusually high or low performance.

Makes ranking selection, reliability, incentive effects, repeated cohorts, and fairness of personnel decisions central. The same reversion benchmark, comparison, uncertainty, language, and decision-hold logic applies.

Use it when actors are selected from rankings or threshold breaches; and performance measures contain substantial noise; and follow-up movement is credited to praise, sanction, or management. Characteristic failures include punishment credited for ordinary rebound, top performer decline moralized, metric gaming, unfair shrinkage.
### Threshold Screening and Reclassification Guardrail

Guard interpretation when eligibility, diagnosis, alarm, inspection, or reclassification begins after one measurement crosses a threshold.

Centers threshold proximity, retest protocols, measurement error, verification timing, and classification consequences. Threshold cases still require explicit selection, reliability, expected reversion, comparison, and decision safeguards.

Use it when measurement noise moves cases across a cutoff; and retesting follows a positive or extreme screen; and crossing back below the threshold is treated as intervention success. Characteristic failures include single-test classification, verification bias, cutoff shopping, false cure claim.
### Policy and Program Extreme-Unit Guardrail

Guard program claims when jurisdictions, sites, schools, teams, or periods are targeted because they recently had exceptional outcomes.

Centers unit-level clustering, secular trend, matched extreme comparators, staggered timing, spillovers, and repeated cohorts. Extreme selection, no-effect reversion, credible comparison, effect uncertainty, and persistence remain the same structure.

Use it when intervention targets worst or best units; and unit outcomes vary over time; and policy success is inferred from post-selection convergence. Characteristic failures include worst-unit rebound credited to policy, comparison contamination, pretrend mismatch, spillover blindness.

`regression_to_the_mean_guardrail`, `extreme_selection_attribution_check`, `reversion_attribution_check`, and `statistical_regression_safeguard` are near names. Repeated measures, controls, shrinkage, simulations, series models, and placebo checks remain mechanisms.

Clinical and Service Recovery Guardrail

distinct_from_parent: The parent also governs performance, policy, quality, screening, and organizational evaluations.

use_when: entry follows symptom or performance crisis

use_when: before-after improvement drives continuation or funding

failure_modes: natural recovery credited entirely to care

name: Performance Reward and Sanction Guardrail

distinct_from_parent: Other applications evaluate treatments, policies, or processes rather than people or units under reward and sanction.

use_when: actors are selected from rankings or threshold breaches

use_when: performance measures contain substantial noise

example_domains: education sports sales workforce_management

failure_modes: punishment credited for ordinary rebound

name: Threshold Screening and Reclassification Guardrail

distinct_from_parent: Selection can also arise from rankings, crises, discretion, or retrospective extreme-case analysis.

use_when: measurement noise moves cases across a cutoff

use_when: retesting follows a positive or extreme screen

failure_modes:
single-test classification
verification bias
cutoff shopping
false cure claim

name: Policy and Program Extreme-Unit Guardrail

distinct_from_parent: Individual and simple measurement settings may not require clustered policy counterfactuals.

use_when: intervention targets worst or best units

failure_modes:
worst-unit rebound credited to policy
comparison contamination
pretrend mismatch
spillover blindness

near_names:
Regression-to-the-Mean Guardrail
Extreme-Selection Attribution Check
Statistical Regression Guardrail