Editorial Independence

one_liner: An evaluative judgment institutionally insulated from the parties it affects.

core_idea:
Editorial independence is the structural pattern by which an *evaluative judgment* is institutionally insulated from the *parties whose interests turn on the conclusion of that judgment*. The defining commitment is not neutrality of values nor freedom from accountability; it is the existence of a deliberate boundary — codified in appointment, funding, jurisdiction, or operational separation — such that those who would gain from a particular verdict cannot, by design, influence the verdict through normal channels.

The pattern is general within institutional substrates. Wherever a system relies on a judgment whose *credibility* matters and where some party has standing to benefit from biasing it, an independence boundary must exist or the judgment's signal value collapses. The boundary's design is itself structural: it must name what judgment is to be protected, identify the influence channels worth blocking, erect institutional firewalls — appointment terms, funding ringfences, blind procedures, operational separation — costly enough that compromise is unattractive, and preserve a separate accountability channel that does not run through the parties being insulated against.

What changes when one names a system as needing editorial independence is that one starts asking the *boundary* questions rather than the *intentions* questions — not "does this judge intend to rule fairly?" but "does the structure of appointment, tenure, and funding create incentives to rule fairly even if the judge does not intend to?" The prime relocates the analysis from individual virtue to institutional structure, which is what makes it a design discipline rather than an exhortation.

structural_signature:
*the protected evaluative judgment* — *the interested parties with standing to benefit* — *the influence channels* — *the firewall across those channels* — *the alternative accountability channel* — *the credibility user* — *the dual cost (legitimate information also blocked)*

A system requires editorial independence when each of the following holds:

- **A protected judgment.** There is an evaluative judgment whose *credibility* is the thing of value — a verdict, a rate decision, a published finding, an audit opinion.
- **Interested parties.** Some party has standing to benefit from a particular conclusion, and therefore an incentive to bias the judgment toward that conclusion.
- **Influence channels.** There are normal channels — appointment, funding, tenure, jurisdiction, social proximity, future employment — through which that interest could translate into bias.
- **A firewall.** The load-bearing element: a deliberate, codified boundary erected across those channels — fixed terms, ringfenced budgets, blind procedures, separation of duties, cooling-off periods — made costly enough that compromise is unattractive. Independence is a property of this structure, not of the evaluator's intentions.
- **An alternative accountability channel.** The judge remains answerable, but through a path that does *not* run through the insulated parties — otherwise insulation becomes unaccountable power.
- **A credibility user.** Some skeptical party relies on the judgment, and whose trust depends on the independence being visible and structural rather than performed.
- **A dual cost.** Every firewall blocks legitimate information as well as illegitimate influence, so independence carries intrinsic failure modes — *captured* (firewalls breached), *ossified* (legitimate accountability blocked), *performed* (firewalls on paper only).

Composed: a credibility-bearing judgment is insulated from interested parties by costly firewalls across the influence channels, while a separate accountability path is preserved — relocating analysis from the evaluator's virtue to the incentive structure, at the deliberate price of some legitimate information.

what_it_is_not:
- **Not `impartiality`.** Impartiality is a *disposition* of the evaluator — even-handedness toward parties; editorial independence is a *structural condition* — firewalls that produce credible judgment even if the evaluator is not impartially disposed. Independence is about incentive structure, not character.
- **Not `procedural_fairness_due_process`.** Due process (the nearest embedding neighbor) governs the *fairness of the procedure* applied to parties — notice, hearing, the right to be heard; editorial independence governs the *insulation of the decider* from interested parties. A scrupulously fair procedure can be run by a captured judge.
- **Not `separation_of_powers`.** Separation of powers divides *functions* across branches so each checks the others; editorial independence insulates a *single evaluative judgment* from the parties it affects. Separation is about distributing authority; independence is about firewalling one judgment.
- **Not `conflict_of_interest`.** A conflict of interest is the *condition* that motivates the need for independence (an interested party with an influence channel); editorial independence is the *structural response* that firewalls that channel. The conflict is the disease; independence is one treatment.
- **Not `regulatory_capture`.** Capture is the *failure mode* in which firewalls are breached and the evaluator serves the interested party; editorial independence is the structure capture defeats. Capture is what happens when independence fails, not an alternative to it.
- **Not `discretion`.** Discretion is the latitude an evaluator has in reaching a judgment; independence is the insulation of that judgment from interested parties. An evaluator can have wide discretion and no independence (captured), or narrow discretion and full independence.
- **Common misclassification.** Crediting an independence claim because the evaluator seems sincere or the procedure looks fair. Catch it structurally: could a strategically motivated party route influence through some unguarded channel (appointment, funding, tenure, assignment)? If so, the independence is *performed*, not real — regardless of virtue or procedural polish.

broad_use:
- **Journalism**: editorial independence from advertisers, owners, and political patrons, from the Chinese wall between advertising and newsroom to public-broadcasting funding ringfences.<!-- FACT-D62-451 -->[^mcchesney-2004]
- **Judicial independence**: tenured judges, irreducible salaries, defined jurisdictions, and the separation of judicial from executive function, as in modern constitutions and the European Convention's Article 6.<!-- FACT-D62-452 -->[^echr-1950]
- **Central bank independence**: appointment terms longer than the political cycle, operational independence on rate decisions, and statutory mandates not revisable by the executive.<!-- FACT-D62-453 -->[^cukierman-1992]
- **Audit and assurance**: external auditors barred from consulting with the auditee, mandatory rotation, and audit committees insulated from management, as under Sarbanes-Oxley and PCAOB regimes.<!-- FACT-D62-454 -->[^sox-2002]
- **Peer review**: anonymous review, conflict-of-interest disclosure, and separation of editor and author roles as the structural condition for the credibility of published claims.<!-- FACT-D62-455 -->[^csiszar-2018]
- **Regulatory agencies**: independent commissions with fixed-term commissioners removable only for cause, ring-fenced budgets, and statutory rulemaking authority.<!-- FACT-D62-456 -->[^breger-edles-2015]
- **Internal investigation and ombuds**: investigators who do not report to the function being investigated — the same boundary problem in miniature.

clarity:
Naming a system as one that requires editorial independence makes visible what would otherwise be invisible: the *interests with standing to compromise the judgment*. It separates the question of who is *making* the judgment from the question of who is *positioned to influence* it, and asks specifically about the boundary between them. This separation is the clarity gain, because it directs attention to a structural relation rather than to the character of the evaluator.

The clarity gain also runs in reverse. A system *claiming* independence that lacks the structural firewalls — or whose firewalls are easy to breach — is correctly diagnosed as *performing* independence rather than having it. The diagnostic does not depend on individual virtue; it depends on incentive structure, which is checkable. One inspects the appointment terms, the funding channels, the removal grounds, and the operational separations, and asks whether a strategically motivated party could route influence through any of them. Where the firewalls are absent or porous, the claim of independence is exposed as decoration regardless of the evaluator's sincerity — and where they are present and costly, independence holds even against an evaluator who might privately prefer to oblige.

manages_complexity:
The pattern compresses an enormous variety of institutional designs — judicial life tenure, central-bank statutes, blind peer review, ombuds reporting lines, audit-committee charters, public-broadcasting trust deeds, regulatory cooling-off periods — onto a single diagnostic: the protected judgment, the threatening interests, the firewall design, and the alternative accountability channel. Otherwise-unrelated arrangements become comparable on the same axes.

This comparability is the complexity reduction. Without the prime, each institutional design is studied in its own domain-specific terms, and lessons do not travel between, say, central banking and peer review. With it, the same intervention vocabulary — longer terms, narrower removal grounds, funding ringfences, conflict-of-interest disclosure, mandatory rotation, double-blind procedures, separation of duties — applies across all of them, so a design move proven in one domain becomes a candidate in another. The complexity the prime manages is the apparent diversity of institutions that protect evaluative credibility; it manages that complexity by revealing them as instances of one four-part structure, each differing only in how its firewalls are tuned to a local threat model.

abstract_reasoning:
Treating independence as a structural condition rather than a virtue of agents enables principal-agent reasoning about *which* firewalls are load-bearing in a given setting. The question "what minimum set of independence properties does this judgment need for its signal to be credible to a skeptical user?" can be answered by enumerating influence channels and asking which ones a strategic compromiser would attack first. The analysis becomes adversarial and concrete rather than aspirational.

It also exposes the *cost* of independence, which is intrinsic and not incidental: every firewall blocks legitimate information as well as illegitimate influence. Tenured judges are harder to remove for misconduct *and* for political pressure; ring-fenced central-bank budgets resist political starvation *and* democratic accountability. The pattern thus suggests its own characteristic failure modes — independence *captured*, where firewalls are breached by stealth; independence *ossified*, where firewalls block legitimate accountability; and independence *performed*, where firewalls exist on paper but not in practice. Reasoning through the prime therefore connects the design of a firewall to the dual costs it imposes, and forces the designer to choose, deliberately, how much legitimate information to sacrifice to block illegitimate influence.

knowledge_transfer:
The structural interventions transfer directly across institutional substrates. *Term lengths beyond the patron's reach* run from judicial life tenure to central-bank decade terms to staggered public-broadcasting boards, sharing the logic of making the patron unable to credibly threaten removal during the patron's own decision-relevant period. *Funding ringfences* run from judicial budgets protected from line-item veto to central-bank seigniorage to public-broadcasting trust deeds to audit fees set by an independent committee, each blocking the same channel of starvation-as-influence. *Blind procedures* run from double-blind peer review to blind grading to blind audit sampling, each blocking the channel by which knowing who is being judged shapes how they are judged. *Separation of duties* keeps auditor and consultant apart, since compositions of independent functions remain independent only when the functions do not consume each other's outputs. And *cooling-off periods* bar regulators from joining the regulated, journalists from accepting gifts, and judges from taking cases involving prior clients, each blocking a temporal channel of influence.

What makes these transfers genuine is the interchangeability of structural roles. *The protected judgment* whose credibility is defended, *the interested parties* with standing to benefit from a particular conclusion, *the influence channels* through which interest could translate into bias, *the firewall* erected across those channels, *the alternative accountability channel* by which the judge remains answerable without flowing through the insulated parties, *the credibility user* whose trust depends on visible independence, and *the failure modes* of capture, ossification, and performance — these map one-to-one across journalism, the judiciary, central banking, audit, peer review, and regulation. The substrate base is strictly institutional and the normative load is heavy: the pattern presupposes a designated evaluator, interested parties, and accountability channels, none of which exists outside human institutions. Within that base, however, the firewalls' specific forms differ in ways predictable from the local threat model, and a designer who carries the four-part structure into a new institution inherits both the diagnostic — name the judgment, the interests, the firewalls, the accountability channel — and the shared intervention vocabulary that has been refined across every domain where an evaluation must be insulated from the parties it affects.

formal_abstract:
Central-bank independence is the cleanest analytic instance, because its rationale is a formal principal-agent argument about *time-inconsistency* that names every role of the prime.<!-- FACT-D62-457 -->[^kydland-prescott-1977] The *protected judgment* is the monetary-policy decision — the interest-rate setting whose credibility is the thing of value. The *interested party* is the incumbent government, which has standing to benefit from a particular conclusion: lower rates and looser policy before an election produce a short-run boom, even at the cost of later inflation.<!-- FACT-D62-458 -->[^nordhaus-1975] The *influence channels* are the normal levers a government holds over an agency — appointing the central-bank governor, setting and threatening the budget, and reappointing (or not) at the end of a term. The structural argument is that if these channels are open, the public *rationally expects* the bank to inflate when pressured, so inflation expectations rise and become self-fulfilling regardless of what the bank actually intends — the judgment's signal value collapses before any decision is made. The *firewall* is the codified set of insulations engineered to close those channels and make the no-inflation commitment credible: appointment terms deliberately *longer than the electoral cycle* (so the government cannot credibly threaten removal during its own decision-relevant period), *operational independence* on the rate decision (the executive cannot overrule a specific vote), a *ringfenced budget* funded from seigniorage rather than annual appropriation, and a *statutory mandate* not revisable by ordinary legislation.<!-- FACT-D62-459 -->[^rogoff-1985] The *alternative accountability channel* preserves answerability without running through the insulated party — the bank reports to the legislature, publishes minutes, and is bound by a published inflation target — so insulation does not become unaccountable power.<!-- FACT-D62-460 -->[^blinder-1998] And the *dual cost* is explicit and intrinsic: the same firewalls that block political starvation also blunt democratic accountability and can *ossify* the bank against legitimate redirection in a genuine emergency. The intervention the prime frames is exactly the design calculus: enumerate the channels (appointment, budget, tenure), and for each ask whether a strategically motivated government could route influence through it, then erect a firewall costly enough to make that route unattractive — while deliberately choosing how much legitimate accountability to sacrifice.

**Mapped back:** Central-bank independence instantiates the full signature — a credibility-bearing judgment, an interested government, named appointment/budget/tenure influence channels, costly firewalls (long terms, operational independence, ringfenced funding), a separate reporting-and-mandate accountability path, and the intrinsic dual cost of blunted democratic control — with the time-inconsistency argument showing *why* the firewall must be structural rather than a matter of the governor's good intentions.

applied_industry:
Judicial independence and external-audit independence are the same four-part structure realized in the courtroom and in the corporate boardroom. For the judiciary, the *protected judgment* is the verdict; the *interested parties* are litigants — and, dangerously, the executive itself when the government is a party or has a stake in the ruling. The *influence channels* are appointment, removal, salary, and case assignment. The *firewalls* are the classic guarantees: *secure tenure* (life appointment or removal only for cause by a supermajority, so a displeased executive cannot fire a judge for an unwelcome ruling), *irreducible salary* (compensation that cannot be cut as punishment), and *defined jurisdiction* insulating which cases a judge hears from political gerrymandering.<!-- FACT-D62-461 -->[^hamilton-1788] The *alternative accountability channel* is appellate review and judicial-conduct boards — the judge is answerable, but through the legal hierarchy, not through the litigants.<!-- FACT-D62-462 -->[^shetreet-forsyth-2012] The *failure modes* the prime names are diagnostic: a captured judiciary (firewalls breached by court-packing), an ossified one (tenure protecting incompetence with no removal path), or a performed one (formal independence on paper while assignment and promotion are quietly controlled). External audit transposes the identical shape into assurance: the *protected judgment* is the audit opinion on the financial statements; the *interested party* is the management being audited, which benefits from a clean opinion; the *influence channels* are the audit fee, the lucrative consulting work the same firm might want, and the personal relationships of a long engagement. Post-Enron reforms (Sarbanes-Oxley) are precisely firewall design: *barring the auditor from consulting* for the same client (separation of duties, so the judgment is not compromised by a larger commercial relationship), *mandatory partner rotation* (a temporal firewall against captured familiarity), and an *audit committee of independent directors* that hires the auditor so management cannot, closing the appointment channel.<!-- FACT-D62-463 -->[^sox-2002] The transferable audit is identical across both: name the judgment, list who benefits from biasing it, trace every channel (fee, tenure, appointment, assignment), and ask whether a strategic compromiser could route influence through any unguarded one — then judge whether the firewalls are costly and real, or merely *performed*.

**Mapped back:** Judicial tenure-and-salary guarantees and Sarbanes-Oxley audit firewalls are the same prime — a credibility-bearing judgment insulated from interested parties across named influence channels, with a separate accountability path and the capture/ossify/perform failure triad — so the design discipline (enumerate channels, erect costly firewalls, preserve alternative accountability) transfers directly across the judicial and audit substrates.


Effect Size

type: prime

one_liner: Magnitude of effect.

status: draft

core_idea: **Effect size quantifies the magnitude of a relationship or difference — the size of an observed effect in substantive, interpretable units — independent of sample size and separately from statistical significance.** The core insight is that statistical significance ("does this effect exist?") is a question about whether an observation deviates from zero; effect size ("how big is this effect?") is a question about the scale of that deviation in units that matter for practical decision-making. Effect size reporting shifts the analytical frame from the dichotomous significance-testing question to the continuous estimation question, revealing a phenomenon that significance testing obscures: a large sample can make any non-zero effect "statistically significant," while a small sample can render even a large practical effect "non-significant," making significance alone an unreliable guide to importance. **The abstraction is that meaningful inference requires attending to magnitude (the effect-size estimate), uncertainty (confidence interval or posterior distribution), and direction (positive or negative) jointly — not collapsing them into a binary reject/do-not-reject decision.**

structural_signature: Effect size operates at the interpretive layer of statistical analysis, with six defining structural roles: *the magnitude-of-effect quantification in substantive units*, *the standardization mechanism dividing observed difference by variability*, *the dimensionless comparability property enabling cross-study synthesis*, *the separation-of-magnitude-from-uncertainty design*, *the meta-analytic pooling foundation replacing p-value vote-counting*, and *the practical-significance-versus-statistical-significance distinction*. Standardized effect sizes (Cohen's d, Pearson's r, odds ratios, η²) divide observed differences by variability measures (standard deviation for d, standard error for proportions), producing scale-free numbers comparable across studies and outcome measures; unstandardized effect sizes (raw mean differences, absolute risk reductions, percentage-point changes) preserve the substantive units that stakeholders care about. The critical structural distinction is the separation of magnitude from reliability: a point estimate of effect size (the "how big") is paired with an interval estimate (the "how certain"), and both are needed for complete inference. Power analysis, sample-size planning, and meta-analysis all hinge on effect-size reasoning: sample size calculations are not fundamentally about achieving "statistical significance" but about engineering sufficient statistical power to detect effects of a specified practical magnitude; meta-analyses combine effect-size estimates across studies to produce pooled estimates and assess heterogeneity, a synthesis that would be impossible using p-values alone.

what_it_is_not:
- **Not the same as statistical significance** — A statistically significant effect can be tiny and practically irrelevant; a large effect can be non-significant in a small study. The distinction between #435 (statistical_significance_p_value) and effect size is fundamental: statistical significance addresses "is this distinguishable from zero?" while effect size addresses "how much is it?" These are orthogonal questions; conflating them has been the systematic error of significance-testing culture.
- **Not a substitute for confidence intervals** — A point estimate of effect size without calibrated uncertainty is incomplete. Effect size must be reported alongside interval estimates (#436 confidence_intervals) to communicate both magnitude and precision. A large effect from a small, noisy study is uninformative without its wide confidence interval; conversely, a narrow interval around a trivial effect communicates different information than a wide interval around the same point estimate.
- **Not uniquely defined** — Different metrics (Cohen's d, Pearson's r, odds ratio, η², relative risk, raw difference) highlight different aspects of the same phenomenon and are not always mutually convertible without assumptions. The choice of metric should depend on the research question and the audience; a meta-analyst may prefer standardized d for cross-study synthesis, while a clinician may prefer number-needed-to-treat for decision-making.
- **Not inherently interpretable without context** — Cohen's d=0.2, 0.5, 0.8 as "small/medium/large" conventions are universal approximations that apply poorly in many domains. A d=0.2 in a preventive intervention on educational outcomes is often substantial; the same d=0.2 in laboratory physics might be negligible. Effective effect-size interpretation requires domain-specific judgment about what magnitudes are practically meaningful.
- **Not a causal claim by itself** — Effect size quantifies an observed association or difference in data; whether that difference is causal depends on design features (randomization, control arms, measured and unmeasured confounding assessment). An effect size from an observational study is a descriptive quantity; causal interpretation requires additional argument.
- **Not identical to clinical or practical significance** — In medicine and policy, clinical significance combines effect size with costs, harms, patient values, and implementation feasibility. A small effect size might have enormous clinical significance if the harm prevented is severe and the intervention is cheap; conversely, a large effect size might have trivial significance if harm prevented is rare or intervention cost is prohibitive.
- **Not monotonic with practical value** — A small standardized effect (d=0.1) applied to millions of people (e.g., a 0.2% reduction in mortality across a large population) can create enormous aggregate value; a large standardized effect (d=0.8) in a small population may matter little in absolute terms. Aggregate impact depends on both effect size and population scale.
- **Not equivalent to #437 (statistical_power)** — While power analysis is built on effect-size thinking (power to detect an effect of specified magnitude), reporting the observed effect size does not tell you the power of the study. A non-significant finding with a large confidence interval excludes neither "no effect" nor "large effect"; determining what the study could have detected requires retrospective power analysis or confidence-interval examination.

broad_use:
**Psychology and Behavioral Science.** The American Psychological Association's Publication Manual (6th edition, 2010, and 7th edition, 2020) explicitly mandates effect-size reporting for all statistical tests. Cohen's d, η² (eta-squared), ω² (omega-squared), and partial η² are standard effect-size metrics. The shift was gradual but decisive: a 1989 survey by Cohen found fewer than 2% of published psychology papers reported effect sizes beyond implicit p-values; by 2010, major psychology journals had adopted formal effect-size reporting requirements. Contemporary psychology distinguishes standardized d (for cross-study meta-analysis) and unstandardized raw differences (for interpretability within a study), with reporting guidance favoring both<!-- FACT-D24-061 -->[^wilkinson-1999].

**Medical Research and Clinical Trials.** Odds ratios, relative risks, hazard ratios, and absolute risk reductions (with number-needed-to-treat, or NNT) are conventional primary-result effect-size metrics in clinical trials. Meta-analyses aggregate effect sizes across trials to produce pooled estimates that inform clinical guidelines and evidence-based medicine syntheses. CONSORT (Consolidated Standards of Reporting Trials) explicitly requires effect-size reporting alongside p-values and confidence intervals. The medical tradition emphasizes communicating effect size in absolute terms (NNT for harm prevention, cost per life-year saved) alongside relative metrics, serving decision-making by patients, clinicians, and regulators<!-- FACT-D24-062 -->[^sullivan-feinn-2012].

**Educational Research and Evidence Synthesis.** The What Works Clearinghouse, a US Department of Education evidence-classification system, uses effect sizes in standard deviation units as its primary classification metric, assigning studies to evidence tiers based on effect magnitude. John Hattie's meta-analytic synthesis of approximately 800 meta-analyses (2008 *Visible Learning* and subsequent updates) identifies effect sizes of 0.4 in standard deviation units as a heuristic threshold for "significant positive impact," with effect sizes above and below providing continuous scaling of intervention effectiveness. This application has driven effect-size awareness in education policy and practice<!-- FACT-D24-063 -->[^glass-1976].

**Economics and Econometrics.** Elasticities (percent-change-in-outcome per percent-change-in-predictor), marginal effects (partial derivatives of outcome with respect to predictors), and dollar-valued impacts are the substantive quantities of interest in economic analysis. T-statistics and p-values play secondary roles as statistical diagnostics; the economic question is always "by how much," not just "is there an effect." Regression-coefficient reporting with confidence intervals and effect-size interpretation based on economic context (e.g., a 0.1 percentage-point change in GDP growth is a very large effect) is standard practice<!-- FACT-D24-064 -->[^funder-ozer-2019].

**Technology, A/B Testing, and Online Experimentation.** Percent-lift (percentage-change relative to control) and absolute-difference effect sizes are the primary decision drivers in A/B testing at technology companies. A test may show statistical significance at p<0.01, but if the lift is 0.1% on a metric worth millions, the practical magnitude may justify investment in implementation. Multi-armed bandit algorithms and Bayesian testing frameworks increasingly orient around posterior distributions of effect sizes rather than dichotomous reject/do-not-reject outcomes, allowing sequential decision-making under specified uncertainty. This shift reflects the recognition that business decisions depend on effect magnitude, not on significance thresholds<!-- FACT-D24-065 -->[^cumming-2014].

**Policy Analysis and Cost-Effectiveness.** Effect sizes expressed as cost-effectiveness ratios (cost per quality-adjusted life year gained), return-on-investment metrics, or cost-benefit comparisons allow comparisons across diverse policy interventions. A job-training program's effect on earnings, measured in dollars per enrollee, is directly comparable to a health-care subsidy's effect on disease incidence. This application of effect-size reasoning aligns with public-finance and welfare-economic frameworks where the magnitude of impact and its cost-efficiency determine policy adoption<!-- FACT-D24-066 -->[^funder-ozer-2019].

**Systematic Review and Meta-Analysis.** Cochrane reviews, Campbell Collaboration systematic reviews, and other high-standards evidence-synthesis programs emphasize effect-size estimation as the central inferential task. Rather than vote-counting (how many studies show significance?), which is biased toward large studies and ignores effect magnitudes, meta-analysis pools effect-size estimates across studies, produces a pooled estimate with confidence interval, and assesses heterogeneity (I² statistics, prediction intervals) to characterize the distribution of effects across contexts. This methodological standard has been adopted across medical, educational, psychological, and policy research<!-- FACT-D24-067 -->[^hedges-olkin-1985].

clarity: Effect-size reporting clarifies what a study actually found by integrating magnitude, precision, and statistical distinguishability into a coherent narrative. The statement "treatment reduced symptoms by 2.3 points on a 20-point scale (95% CI 1.1–3.5, p=0.002)" communicates the point estimate (2.3), the uncertainty bounds (1.1 to 3.5), and the statistical distinguishability (p=0.002 from zero), allowing readers to judge whether the effect is practically meaningful and to visualize the range of plausible effects. The same result stated as "treatment was significantly better than control (p<0.01)" suppresses the magnitude and precision information, leaving readers unable to assess practical importance. In meta-analysis, effect-size reporting enables pooling across studies using different sample sizes, outcome measures, and designs; "vote counting" (counting how many studies achieved p<0.05) is biased toward studies with large samples and toward dichotomizing a continuous quantity (effect magnitude) into a binary outcome (significant/non-significant). Effect-size discipline also clarifies interpretation of null results: a non-significant result with a narrow confidence interval (e.g., d with 95% CI -0.1 to +0.1) is evidence of a small effect; a non-significant result with a wide confidence interval (e.g., d with 95% CI -0.8 to +0.8) is inconclusive, leaving open the possibility of a large effect. This distinction is invisible in pure significance testing but central to interval-based effect-size reporting<!-- FACT-D24-068 -->[^cumming-2014].

manages_complexity: Effect size provides a unified currency for comparing findings across studies, measures, contexts, and interventions, reducing the apparent incommensurability of diverse evidence. A meta-analyst combining psychotherapy trials measuring depression on different scales (Beck Depression Inventory, Hamilton Depression Rating Scale, Patient Health Questionnaire-9) cannot directly pool raw scores (incompatible metrics) but can pool Cohen's d effect sizes, each computed as the standardized mean difference on its native scale. Meta-regression extends this framework by examining whether effect sizes covary with study characteristics (sample size, demographic composition), trial design features (intensity, duration), or patient-population features (baseline symptom severity, comorbidity), producing moderator analyses that would be impossible with p-values alone. In applied settings, effect-size thinking allows decision-makers to compare interventions on a common scale: a 0.1-SD improvement in educational outcomes may appear less impressive than a 0.5-SD effect from another intervention, but if the first is dramatically cheaper to implement and targets a priority population, its cost-effectiveness can be competitive. The complexity management lies in standardizing the metric (converting diverse raw outcomes into dimensionless effect sizes) while preserving uncertainty through paired interval estimates— a discipline that has consistently improved scientific communication and organizational decision-making where it has been systematically adopted<!-- FACT-D24-069 -->[^hedges-olkin-1985].

abstract_reasoning: Effect-size reasoning embodies a fundamental abstraction about the structure of inference: *the statistical question "is there an effect" is distinct from and less important than the substantive question "how big is it, is it stable across contexts, and does its magnitude matter for my decisions?"* This perspective shifts analytical orientation from binary hypothesis testing (reject/do-not-reject) to continuous estimation with quantified uncertainty. The abstraction applies far beyond formal statistics: in engineering, setting specifications and tolerances is about magnitude and precision, not about hypothesis tests; in manufacturing quality control, statistical process control charts monitor the centering and spread of a process, not just whether it's "in control"; in medical monitoring, tracking a patient's marker trajectory requires continuous magnitude assessment, not sequential binary tests. The "statistically significant = real and important; non-significant = null and negligible" mental shortcut fails in two distinct ways: (a) with huge samples, it calls tiny effects that are practically negligible "statistically significant," leading to resource allocation toward trivial findings; (b) with small samples, it calls practically meaningful effects "non-significant," leading to premature abandonment of potentially valuable interventions. Effect-size discipline prevents both failures by centering magnitude as the primary inferential object<!-- FACT-D24-070 -->[^funder-ozer-2019]. The deeper principle is that effective decision-making under uncertainty requires attending to magnitude (the point estimate), direction (positive or negative), uncertainty (confidence or credible interval), and context (what counts as practically meaningful) jointly — collapsing these into a binary decision discards critical information needed for good judgment.

knowledge_transfer:
| Domain | Typical Effect-Size Metric | Interpretive Context |
|--------|---------------------------|---------------------|
| Psychology | Cohen's d, η², partial η² | d=0.2 small, 0.5 medium, 0.8 large (Cohen's conventions) |
| Medical trials | Odds ratio, relative risk, HR, NNT | OR=1.5 modest; NNT provides absolute scale |
| Epidemiology | Risk difference, hazard ratio | Absolute risk reduction preferred for clinical communication |
| Education | Effect size in SD units (Glass's Δ) | 0.1 small, 0.25 moderate, 0.4 large (What Works / Hattie) |
| Economics | Elasticity, marginal effect, $-impact | Unit-native; economic interpretation context-specific |
| A/B testing | Percent lift, absolute difference | Context-specific thresholds (e.g., 1% lift on revenue) |
| Marketing | Lift, ROAS, incremental conversion | Dollar-valued; comparable across campaigns |
| Meta-analysis | Pooled d, pooled OR with heterogeneity | Combined across studies with I² for heterogeneity |
| Machine learning | Accuracy/AUC improvement, F1 change | Task-specific; minimum meaningful lift varies |
| Ecology | Effect size on population growth rate, diversity | Biologically relevant thresholds; scaled to life-history |

prose:
_(Pass B — deferred)_

### Catalog cross-references (v1, batches 02–29)

Solution archetypes that reference this prime (per `solution_archetypes/_catalog/catalog_v1_batches_02_29.yaml`):

**As related prime (2):** `catalytic_pairing`, `interaction_effect_mapping`

notes: Effect-size reporting is now recognized as a foundational practice of modern statistical inference, emphasized across the American Statistical Association's 2016 and 2019 statements, journal guidelines (APA Publication Manual, CONSORT, STROBE), and funding-agency requirements. The practical and philosophical shift from significance testing to effect-size-plus-interval reporting remains incomplete in many fields, but momentum is strong in medicine, psychology, economics, and policy research. Contemporary practice emphasizes: (a) pre-specified minimum effects of interest for sample-size planning; (b) confidence intervals or credible intervals alongside point estimates; (c) effect-size reporting in abstracts and headline findings, not relegated to tables; (d) domain-specific interpretation rather than universal benchmarks; (e) heterogeneity assessment in meta-analytic synthesis rather than averaging across diverse effects; (f) Bayesian posterior distributions of effect size as alternative to frequentist point-and-interval pairs. The tight structural connection to #437 (statistical_power) means the two should be traversed together: power analysis is effect-size analysis given sample size, and the two address complementary aspects of research design and interpretation. Meta-science research documents that fields which moved toward effect-size discipline (clinical medicine, education research) have achieved better cumulative evidence synthesis than fields that remained significance-testing focused.


Efference Copy

one_liner: Routing an internal copy of a command to the perceiver so self-caused effects can be predicted and subtracted, leaving only the world-caused remainder.

origin_domain: neuroscience

core_idea:
Efference copy names the recurring structural pattern in which a system
that issues a control command also routes an *internal copy* of that
command to its perception, monitoring, or audit subsystem, which uses the
copy to predict the self-caused consequences and subtract them from the
incoming signal, leaving only the world-caused remainder for further
processing. The structural commitment is the *separation of self-caused
effects from world-caused effects via a forward signal broadcast at the
moment of action* — not via retrospective inference.

The pattern requires five jointly necessary elements: a *controller* that
issues commands to effectors; an *effector* that executes the command and
changes the world or the controller's own state in ways the perceiver will
register; a *perceiver or monitor* that senses both world-caused and
self-caused changes through the same channel, with no intrinsic way to
tell them apart from the raw signal; an *internal copy* of the command,
broadcast concurrently from controller to perceiver and bypassing the
effector path; and a *predictor and subtractor* that uses the copy to
anticipate the self-caused component of the upcoming sensation and
removes, attenuates, or flags it before downstream processing. The
diagnostic signature is *self-attenuation*: predicted self-caused signals
are systematically smaller than identical world-caused signals, because
the predictor cancels them, and when the prediction is wrong — effector
drift, unexpected load, interference — the residual is unattenuated and
downstream systems see the discrepancy. This is distinct from generic
feedback, which corrects after the world responds, and from broad
predictive comparison, because efference copy is specifically the
*self-versus-world attribution* mechanism built from broadcasting a
command copy to the perceiver.

structural_signature:
*the controller issuing commands* — *the effector executing them and changing the sensed world* — *the perceiver sensing self- and world-caused changes through one channel* — *the internal command copy broadcast concurrently, bypassing the effector path* — *the predictor-and-subtractor that cancels the anticipated self-effect* — *the self-attenuation signature with unattenuated residual on prediction error*

A configuration exhibits efference copy when each of the following holds:

- **A controller.** Some component issues control commands to effectors.
- **An effector.** It executes the command and changes the world, or the
  controller's own state, in ways the perceiver will register.
- **A perceiver with a single channel.** A monitor senses both world-caused and
  self-caused changes through the same channel, with no intrinsic way to tell
  them apart from the raw signal.
- **An internal command copy.** A copy of the command is broadcast concurrently
  from controller to perceiver, *at the moment of action* and bypassing the
  effector path — a forward signal, not a retrospective inference.
- **A predictor and subtractor.** The perceiver uses the copy to predict the
  self-caused component of the upcoming sensation and removes, attenuates, or
  flags it before downstream processing.
- **A self-attenuation signature.** Predicted self-caused signals are
  systematically smaller than identical world-caused signals; when the
  prediction is wrong (effector drift, unexpected load), the residual is
  unattenuated and downstream systems see the discrepancy.

Composed, these separate self-caused from world-caused effects via a forward
broadcast rather than after-the-fact correction — distinguishing the pattern
from generic feedback (which corrects after the world responds), from plain
self-monitoring (no concurrent copy), and from broad signal extraction (the
separated component need not be self-caused). Attribution bugs live at the
forward channel, not at the sensor or controller alone.

what_it_is_not:
- **Not `feedback`.** Feedback corrects *after* the output deviates and the
  world responds; efference copy broadcasts a command copy *concurrently with
  action*, predicting the self-effect before it arrives — forward, not
  retrospective.
- **Not `signal_extraction`.** Signal extraction separates any component of
  interest from noise; efference copy specifically separates the *self-caused*
  component using a command copy — the distinguished component must be
  self-generated.
- **Not `observer_effect`.** The observer effect is measurement disturbing the
  observed; efference copy is the *cancellation* of self-caused disturbance via
  a forward prediction, the opposite move.
- **Not plain self-monitoring.** Self-monitoring observes one's own state but
  need carry no concurrent command copy; efference copy's defining element is
  the broadcast-at-the-moment-of-action forward channel.
- **Not `mere_exposure_effect`.** That is a familiarity-driven preference
  shift; efference copy is a sensorimotor attribution mechanism — the embedding
  proximity is incidental.
- **Not predictive coding in general.** Broad predictive comparison anticipates
  any input; efference copy is the specific *self-versus-world attribution*
  built from broadcasting a command copy to the perceiver.
- **Not `latency` management.** Latency concerns delay per se; efference copy
  addresses the *attribution* problem that delay creates (delayed-self versus
  new-world), not the delay itself.
- **Common misclassification.** Responding to "the system keeps reacting to its
  own actions" by upgrading the sensor. That treats an *attribution* failure as
  a *signal-to-noise* one; the fix is a forward channel, and sensor quality is
  irrelevant.

broad_use:
The canonical instance is neuroscience and motor control: corollary
discharge in oculomotor circuits, saccadic suppression during eye
movements, the cerebellar forward model for limb dynamics, and the
attenuation of self-produced touch all instantiate the broadcast-and-subtract
architecture.<!-- FACT-D62-476 -->[^sperry-1950] It recurs in speech production, where speakers attenuate
perception of their own voice via command-driven predictions in auditory
cortex and compensate when feedback is artificially altered.<!-- FACT-D62-477 -->[^houde-nagarajan-2011] It recurs in
control engineering as the Smith predictor and internal model control:<!-- FACT-D62-478 -->[^smith-1957]
to control a plant with significant delay, the controller maintains an
internal model and operates on the prediction error rather than the raw
output.<!-- FACT-D62-479 -->[^smith-1957] It recurs in robotics, where a forward model predicts the expected
reaction force of a commanded torque and subtracts it, leaving only the
unexpected component for collision detection.<!-- FACT-D62-480 -->[^haddadin-etal-2017] It recurs in security
operations, where a scheduled maintenance command emits a concurrent
notification so the monitoring system anticipates and cancels the
self-caused changes. It recurs in software change management, where a
deploy emits an expected-effect manifest in parallel with the change, so
the observability system attenuates the expected signature and alerts only
on deviations. And it recurs in organizational accountability, where an
announced policy change is in effect an efference copy to the audit
function, so downstream metric movements are read as expected rather than
anomalous.

clarity:
Efference copy separates two phenomena that the raw signal cannot:
world-caused changes that require action and self-caused changes that do
not. Without the pattern, every signal looks like something happening *to*
the agent; with it, the agent perceives itself as a distinct cause and
reserves its alarm response for genuinely external events. It also
separates the *prediction* from the *correction*: the forward copy
predicts the expected sensation, the actual sensation arrives, and the
comparison yields a residual — a three-part structure crisper than
"self-monitoring" or "internal model" alone, both of which collapse the
prediction-versus-residual distinction. And it separates two scalars that
are routinely confused: *signal-to-noise*, a ratio property of the sensor
improved by better sensors, and *self-versus-world attribution*, a
predictive property of the perceiver improved by better efference copy. A
sensor swap will not fix an attribution failure, and naming the pattern is
what makes that non-obvious fact visible.

manages_complexity:
The pattern compresses a substrate-spanning design problem into a single
named architecture: broadcast the command sideways at the moment of issue,
and subtract the predicted self-effect from the incoming signal before
downstream processing. Engineers meeting the distinguish-self-from-world
problem in a new domain need not re-derive the solution; they can run the
five-part diagnostic — where is the controller, the effector, the monitor,
the forward channel, the predictor — and locate the intervention point. By
factoring attribution into these five components, the pattern lets an
analyst reason about where an attribution failure must lie without
modelling the whole sensorimotor or monitoring loop, and it cleanly
separates the question of sensor quality from the question of predictive
cancellation, which are otherwise entangled under a vague heading of
"the system keeps reacting to its own actions."

abstract_reasoning:
Recognising the pattern supports inference about systems that lack it. The
*failure mode of absent efference copy* is that a system cannot
distinguish its own contributions from external ones, so it either
over-alerts on every self-action or learns to silence whole sensor
channels, losing world-attributable signal as well — both failure modes
carrying distinct signatures. The *Smith-predictor argument* holds that a
system acting under perceptual delay performs better with a forward model
than with raw feedback alone, because feedback cannot disentangle
delayed-self from new-world.<!-- FACT-D62-481 -->[^smith-1957] The *saccadic-suppression argument* holds
that any system moving its own sensor must solve the self-motion-versus-world-motion
problem, and the cleanest solution is a command copy to the processor.<!-- FACT-D62-482 -->[^sommer-wurtz-2008]
The pattern even illuminates pathology: a failure of efference copy in
inner-speech monitoring has been proposed as a mechanism for misattributing
self-generated signal to an external source, and the structural inference<!-- FACT-D62-483 -->[^feinberg-1978]
— self-signal not cancelled, therefore experienced as world-signal —
recurs in any system whose attribution layer fails. The unifying
structural insight is that attribution bugs live at the forward channel,
not at the sensor or the controller individually.

knowledge_transfer:
Because the self-versus-world attribution problem is substrate-independent
and the forward-copy-plus-predictor-plus-subtractor solution is its
substrate-independent answer, the architecture transfers cleanly across
domains. The Smith predictor is the control-engineering instantiation of
the cerebellar forward model: both compute what the sensor should be
reading if the command is executing as intended, and both act on the
residual. The saccadic-suppression shape transfers to change-window
suppression in monitoring systems, where a maintenance window's
announcement *is* the efference copy and the reduced sensitivity during
the window is the attenuation, carrying the intervention vocabulary —
precise timing, expected-effect model, unattenuated residual — across the
gap. Forward-model torque cancellation in robotics is the same structure
as vestibulo-ocular compensation.<!-- FACT-D62-484 -->[^wolpert-etal-1995] The practice of emitting an
expected-effect manifest before a deploy, so observability tools attribute
consequent metric movement correctly, is structurally the efference-copy
move in software. In every port the diagnostic is the five-part
decomposition and the prescription is the same: install a concurrent
forward channel from the actor to the monitor, predict the self-effect,
and subtract it before alarms fire. The transfer carries its boundary as
well: a receiving domain must distinguish efference copy from reactive
feedback (which corrects after the response arrives), from generic
self-monitoring (which need not use a concurrent command copy), and from
broad signal extraction (where the separated component is not specifically
self-caused). A practitioner who has built this architecture in one
substrate arrives at the next already asking where the controller, the
monitor, and above all the forward channel sit — and already knowing that
when attribution fails, the forward channel is where to look.

formal_abstract:
Saccadic suppression and the oculomotor corollary discharge is the prime's
canonical neuroscience case, and it isolates every role. The *controller* is the
oculomotor circuitry issuing a command to move the eyes; the *effector* is the
extraocular muscles, which sweep the eye and so sweep the retinal image. The
*perceiver* is the visual system, which receives a single channel — the moving
image on the retina — in which a self-caused sweep (the eye moved) and a
world-caused sweep (the scene moved) are intrinsically indistinguishable from
the raw signal. The *internal command copy* is the corollary discharge,
broadcast *concurrently* from the oculomotor command to visual areas, bypassing
the muscle-and-retina path.<!-- FACT-D62-485 -->[^sommer-wurtz-2008] The *predictor and subtractor* uses that copy to
anticipate the retinal smear the saccade will cause and suppress it, which is why
you do not perceive the world lurching every time your eyes dart. The diagnostic
*self-attenuation* signature is exact and testable — the same retinal motion is
perceived when externally imposed (push your eyeball gently and the world *does*
appear to jump) but cancelled when self-generated, because only the
self-generated case carries a matching command copy. And when prediction fails,
the *unattenuated residual* is seen: this is why a system whose eye muscles are
paralysed and tries to move them perceives the world as shifting — the command
copy predicts a sweep that the failed effector never produced.<!-- FACT-D62-486 -->[^vonholst-mittelstaedt-1950]

**Mapped back:** The oculomotor command is the controller, the eye muscles are
the effector, vision is the single-channel perceiver, corollary discharge is the
concurrent command copy, saccadic suppression is the predict-and-subtract, and
the pushed-eyeball jump is the unattenuated residual on prediction error.

applied_industry:
The Smith predictor in control engineering instantiates the same prime in a
process-control substrate, solving the self-versus-world problem under
transport delay. The *controller* is the feedback regulator; the *effector* is
the plant — say a chemical reactor whose temperature responds to a heating
command only after a long pipe-and-mixing delay.<!-- FACT-D62-487 -->[^smith-1957] The *perceiver* is the
temperature sensor, whose single reading cannot tell, at any instant, how much
of the current temperature is the *delayed* result of the controller's *own*
earlier command versus a *new* world disturbance (a feed change). The *internal
command copy* is the controller's internal plant model, run forward
concurrently with the real command; the *predictor and subtractor* computes what
the sensor *should* read if the command is executing as intended and operates on
the *residual* between predicted and actual output. Self-caused, delayed effects
are thereby cancelled, and only the *unexpected* component — the genuine
disturbance — reaches the control law, exactly the prime's broadcast-and-subtract
architecture. The clarifying payoff matches the prime's: a better *sensor* will
not fix instability caused by delay (that is an attribution problem, not a
signal-to-noise one); a forward model will. A structurally identical applied
instance is software deploy management, where a release emits an expected-effect
manifest in parallel with the change, so the observability system attenuates the
predicted metric movement and alerts only on deviations — the deploy notification
playing the role of the command copy.<!-- FACT-D62-488 -->[^beyer-etal-2016]

**Mapped back:** The regulator is the controller, the delayed plant is the
effector, the sensor is the single-channel perceiver, the internal plant model
is the concurrent command copy, operating on the prediction residual is the
predict-and-subtract, and an unexplained residual is the world-caused
disturbance the architecture isolates.


Efficiency

slug: efficiency

one_liner: Judge an input–output arrangement against its feasible alternatives: it is efficient when no alternative preserves or improves the specified result while using fewer resources, and its inefficiency is the dominated slack removable without sacrifice.

origin_domain: synthesized

core_idea:
Efficiency is the relational property of an input–output arrangement that has no dominated slack relative to a specified feasible set. An arrangement is efficient when no feasible alternative can preserve or improve the named result while using less of at least one resource and no more of the others. Equivalently, if an alternative produces at least as much of every protected output with no more of any tracked input, and improves at least one dimension, the current arrangement is inefficient. Its inefficiency is the gap to the undominated frontier: resource use that can be removed, or result that can be gained, without paying for the improvement on another protected dimension.

The abstraction is deliberately narrower than “doing something well.” Every efficiency claim must expose five things: what result is protected; which resources, costs, or losses count; which alternatives are feasible; which constraints define that feasibility; and which dominance or trade-off rule makes one arrangement preferable. Change any one and the frontier can move. A redundant component can be slack in a throughput-only model and indispensable in a reliability-constrained model. A low-energy algorithm can be efficient under an energy budget and inefficient under a latency requirement. Efficiency is therefore not an intrinsic adjective attached to a device or institution; it is a relation among an operating point, a feasible set, and a declared performance frame.

structural_signature:
*the operating arrangement* — *the protected output or result* — *the tracked resources, costs, or losses* — *the constrained feasible set* — *the shared comparison frame* — *the dominance rule* — *the undominated frontier* — *the removable slack or output gap*

- **Operating arrangement:** the actual process, allocation, design, or transformation under review.
- **Protected result:** the output, quality, service level, fitness contribution, or other result that must be preserved or improved.
- **Tracked resources:** time, energy, material, money, memory, attention, risk, or other inputs and losses counted in the verdict.
- **Feasible alternatives:** genuine counterfactual arrangements available under the same regime, not imaginary points produced by omitting constraints.
- **Constraints:** technology, safety, reliability, quality, law, geometry, or other conditions that bound the feasible set.
- **Dominance rule:** an alternative dominates when it is no worse on every protected dimension and strictly better on at least one.
- **Frontier:** the set of undominated feasible points; an efficient arrangement lies on it.
- **Slack or gap:** the recoverable distance from a dominated operating point to the frontier, stated in resources saved or output gained.

what_it_is_not:
- **Not effectiveness.** Effectiveness asks whether the intended result is achieved; efficiency asks whether a feasible alternative could achieve at least that result with less resource or loss. A process can be effective and inefficient, or resource-thrifty and ineffective.
- **Not productivity alone.** Productivity reports realized output per unit input. Efficiency compares the operating point with the best feasible relationship under named constraints; a high ratio can remain inefficient if a feasible alternative is higher.
- **Not optimization.** Optimization is a search or solution procedure over an objective and feasible set. Efficiency is a property or verdict on an arrangement; an evolved, accidental, or heuristically discovered design can be efficient without anyone having optimized it.
- **Not Pareto Efficiency in particular.** Pareto Efficiency is the multi-objective or multi-party species whose frontier forbids improving one protected dimension without worsening another. Generic Efficiency also covers fixed-output input minimization and fixed-input output maximization.
- **Not economy or austerity.** Using less is not efficient if the protected result falls. A cut that lowers reliability, safety, or quality may move to a different output rather than remove slack.
- **Not intrinsic.** No arrangement is efficient without a comparison class, tracked dimensions, and constraints. “Efficient” with those omitted is an incomplete claim.

broad_use:
Engineering evaluates useful energy, work, or signal delivered relative to input and unavoidable loss. Computer science compares algorithms and architectures in time, memory, bandwidth, energy, and accuracy under the same task requirements. Economics and operations use production, cost, and allocation frontiers. Biology compares conversion of nutrients, energy, or reproductive investment into survival and offspring under environmental constraints. Logistics compares throughput and service level against distance, handling, inventory, and delay. Organizations compare service quality or decision output against labor, attention, and coordination burden.

These are literal co-instances because each supplies the same roles: an input–output transformation, feasible alternatives, constraints, a dominance relation, a frontier, and a recoverable gap. The units differ; the reasoning does not.

clarity:
Efficiency clarifies by forcing an apparently simple compliment into a checkable relational claim. “This process is efficient” becomes: relative to which alternatives, holding which result constant, counting which resources, under which constraints? That expansion reveals two common errors. The first is effectiveness–efficiency conflation: achieving the goal is mistaken for doing so without avoidable waste. The second is omitted-constraint slack: redundancy, testing, time buffers, accessibility, or safety margin are called waste because the comparison model silently excludes the result they protect.

The frontier framing also separates improvement from sacrifice. Removing dominated slack is an efficiency gain: something improves and nothing protected worsens. Moving along an undominated frontier is a trade-off: one protected dimension improves only because another worsens. Calling both “efficiency” hides the choice. The distinction makes the intervention boundary explicit—eliminate slack, but adjudicate trade-offs.

manages_complexity:
A real arrangement can consume many inputs and produce many outputs. Efficiency compresses that high-dimensional field into a small geometry: a feasible set, its undominated frontier, and the current point's relation to it. Instead of tracking every implementation detail, the analyst asks whether another feasible point dominates the current one; if so, the difference identifies recoverable slack. If not, further improvement requires moving along the frontier and therefore naming a trade-off rather than promising a free gain.

This compression is powerful because it is conditional. Once a quality, safety, or regime constraint changes, the feasible set and frontier must be recomputed. The abstraction manages complexity without pretending the frontier is timeless or context-free.

abstract_reasoning:
The core move is *fix the protected result and constraints, compare feasible alternatives, then remove only dominated slack*. First state the output or outcome that must not worsen. Second expose every resource or loss that counts. Third define feasible alternatives under the same constraints. Fourth apply the dominance relation. A dominating alternative licenses a genuine efficiency intervention; absence of one means the point is on the current frontier and any further move is a trade-off, a reframing of the outputs, or a change in constraints.

The same sequence supports diagnosis. If two analysts disagree about efficiency, locate the disagreement in the frame: different protected outputs, uncounted inputs, different feasibility assumptions, or different constraints. It also supports prediction: relaxing a constraint can move the frontier outward; tightening a safety or quality requirement can move it inward; technology can change the feasible set; congestion or degradation can make a formerly efficient operating point dominated.

knowledge_transfer:
The transferable structure is stable across substrates. Map the protected output to delivered energy, correct answers, manufactured units, offspring, resolved cases, or transported cargo. Map inputs to joules, time, memory, materials, nutrients, labor, attention, or risk. Map constraints to physical law, algorithmic correctness, technology, safety, ecology, regulation, or service quality. Then construct the feasible set, identify dominance, and distinguish slack removal from movement along the frontier.

The portable caution transfers with equal force: never export an efficiency verdict without its frame. A manufacturing benchmark can teach a hospital to look for dominated process slack, but not to treat quality and resilience as if they were interchangeable with unit cost. What transfers is the frontier method and the requirement to preserve the receiving domain's protected outputs and constraints.

notes: Authored when the mixed-DAG pass found Productive Efficiency explicitly referring to a missing generic Efficiency parent. The scope is the feasible-set and dominance relation, not the unrestricted adjective “efficient.”


Efficiency Rebound

one_liner: An efficiency gain lowers the effective cost per unit of an activity, inducing enough additional demand to offset part or all of the resource saving expected at fixed activity volume.

core_idea:
Efficiency rebound occurs when an input-output improvement reduces the resource
required per unit of service and, through the same improvement, lowers the
service's effective unit cost. Activity then expands. The engineering
calculation predicts saving at the old volume; the system calculation multiplies
the new lower resource intensity by the new higher volume. The gap between those
two savings is the rebound.

The concept spans a continuum. A partial rebound consumes some but not all of
the expected saving. A full rebound leaves aggregate resource use unchanged.
A more-than-full rebound raises total resource use; that sign-flipping region is
Jevons Paradox. The family therefore warns against both naive optimism
(efficiency guarantees aggregate conservation) and naive pessimism (any rebound
means efficiency failed).

structural_signature:
*the resource input* — *the produced service or activity* — *the efficiency
gain* — *the lower resource intensity* — *the fall in effective unit cost* —
*the demand or activity response* — *the fixed-volume saving* — *the realized
aggregate use* — *the rebound fraction*

- **Efficiency gain.** Fewer units of input produce the same unit of service.
- **Effective-cost channel.** Money, time, effort, risk, or another shadow price
  per service unit falls.
- **Expandable activity.** Users, firms, or applications can consume more of
  the now-cheaper service or create uses previously priced out.
- **Aggregate recombination.** Total input equals input per unit multiplied by
  activity volume after response.
- **Graded offset.** Rebound is the fraction of fixed-volume saving consumed by
  additional activity.
- **Boundary condition.** When demand is saturated, inelastic, capped, or
  decoupled from the cost drop, rebound is small and efficiency conserves.

what_it_is_not:
- **Not Jevons Paradox as the whole family.** Jevons is the strong child where
  rebound exceeds 100 percent and total resource use rises. Most measured
  rebounds are partial.
- **Not Withdrawal Rebound.** `withdrawal_rebound` (legacy alias
  `rebound_effect`) requires a sustained suppressor, lagging internal
  compensation, abrupt removal, and transient overshoot. Efficiency rebound
  requires none of those; it is a demand response to reduced effective unit
  cost.
- **Not Counterresponse Offset in general.** `counterresponse_offset` also
  covers safety, insurance, and other improvements that alter exposure without
  improving input-output efficiency.
- **Not an efficiency failure.** The per-unit improvement can be genuine and
  exactly as specified while the aggregate saving is smaller than predicted.
- **Not guaranteed backfire.** A response can be present while most of the
  expected saving survives.
- **Not price elasticity alone.** Elasticity parameterizes a direct response;
  the rebound includes the efficiency change, effective-cost translation, new
  uses, indirect spending, and aggregate resource recombination.

broad_use:
- **Buildings and appliances.** Efficient heating, cooling, lighting, and
  devices lower operating cost and can increase comfort, hours of use, floor
  area served, or adoption.
- **Transportation.** Fuel economy and road-speed improvements lower the cost
  per trip or kilometer and can induce more travel, longer routes, or
  relocation.
- **Water and agriculture.** Efficient irrigation can increase irrigated area,
  cropping intensity, or adoption of thirstier crops.
- **Computing.** Lower energy, money, or time per operation unlocks workloads,
  users, and products that were infeasible at the old cost.
- **Industrial resources.** Process efficiency can lower product cost and
  expand output enough to erode aggregate material or energy savings.
- **Attention and time.** Faster search, filtering, or communication lowers
  effort per item and can expand the number of items processed.

clarity:
The prime installs a level-of-analysis distinction. Component efficiency is a
ratio at fixed output. Conservation is an aggregate result after quantity
adjusts. Reporting the first as proof of the second silently assumes away the
response channel.

It also replaces a binary argument with a coefficient. The relevant questions
are how much effective cost fell, which activity margin can expand, over what
time horizon, and what share of expected saving survives. Direct rebound,
indirect re-spending, and economy-wide restructuring mature on different
timescales and should not be collapsed into one early estimate.

manages_complexity:
Heating, traffic, irrigation, and computing differ in technology but share one
product identity:
\[
\text{aggregate input}=\text{input per service unit}\times\text{service volume}.
\]
Efficiency acts on the first term. Rebound acts through the second. This
decomposition localizes policy: efficiency standards improve the intensity
term; absolute caps, input prices, quotas, or budgets constrain the volume or
aggregate term.

The prime also organizes response channels. Direct rebound is more use of the
same service. Indirect rebound is spending the saved money or time on other
resource-using services. Economy-wide rebound includes price, investment,
infrastructure, and innovation changes. Each belongs in the account, but their
evidence and time horizons differ.

abstract_reasoning:
The *fixed-volume counterfactual* computes the saving if activity had not
changed. The *effective-cost audit* identifies the currency—money, time, effort,
risk—in which the service became cheaper. The *latent-demand search* looks
beyond existing users to applications and populations newly feasible at the
lower cost. The *saturation test* asks whether demand can still expand; if not,
efficiency translates much more directly into conservation.

The *policy-pairing inference* follows from the two-factor identity. When an
absolute resource target matters, improve intensity and constrain the resource
or volume channel separately. Tightening efficiency alone enlarges the
fixed-volume saving but does not itself prevent the response that consumes it.

knowledge_transfer:
The same roles recur across energy, water, transport, compute, time, and
attention. Fuel becomes electricity or processor energy; miles become lumen
hours or model inferences; money price becomes waiting time or effort. The
efficiency-to-effective-cost-to-demand-to-aggregate-use chain remains intact.
The transfer is literal wherever service volume can expand in response to a
lower generalized cost.