# FITNESS_REQUIREMENTS_BY_CLAIM_TYPE -- Parameterized Single-Schema

**Owner:** Claim-Typing Engine (S5). **Consumers:** S5, S7 Fitness Adjudicator. **Library:** Public RAM.

Per Phase 3 finding R2.4, fitness requirements are emitted as ONE parameterized schema -- NOT five independent rubrics. What varies across claim types: load-bearing properties, disqualifiers, weight overrides, and fragility flags. The schema itself is fixed.

---

## Shared Schema (fixed across all claim types)

```
fitness_requirements:
  claim_id: <id>
  claim_type: descriptive | causal | predictive | normative | interpretive
  load_bearing_properties: [<property-name>, ...]
  disqualifiers: [<disqualifier-name>, ...]
  weight_overrides: { <dimension>: <multiplier>, ... }
  admiralty_input_treatment: "input-only"   # never judgment (R2.3)
  fragility_flag: none | fragile-normative | fragile-predictive
```

Admiralty A-F/1-6 enters as ONE input among N source-property inputs. It is never treated as the fitness judgment (Capet & Revault d'Allonnes 2019; SANS 2020).

---

## Per-Claim-Type Parameterization

### DESCRIPTIVE
- **load_bearing_properties:** coverage, currency-of-data, authority-of-source, sampling-representativeness.
- **disqualifiers:** stale-data-beyond-domain-half-life; unnamed-source; convenience-sample-generalized-to-population.
- **weight_overrides:** coverage x 1.3; internal-validity x 0.7 (RCTs not required).
- **fragility_flag:** none.

### CAUSAL
- **load_bearing_properties:** internal-validity, identification-strategy, confounding-control, pre-registration, replication.
- **disqualifiers:** NO control for confounding of any kind; observational correlation presented as causation without DAG justification; single non-replicated study carrying a policy claim.
- **weight_overrides:** internal-validity x 1.5; sample-size x 1.2.
- **fragility_flag:** none.
- **Source appraisal:** RoB 2 / ROBINS-I / GRADE.

### PREDICTIVE
- **load_bearing_properties:** out-of-sample-calibration, track-record, Brier-score (where available), forecaster-accountability.
- **disqualifiers:** in-sample-only validation claimed as predictive skill; no calibration curve; no track record on analogous forecasts.
- **weight_overrides:** calibration x 1.5; study-design-prestige x 0.6 (a superforecaster blog with track record > a peer-reviewed model with no validation).
- **fragility_flag:** fragile-predictive (no GRADE-analog; adapted from Brier-score logic).

### NORMATIVE
- **load_bearing_properties:** argumentative-coherence, reflective-equilibrium, practitioner-framing-capacity, context-mechanism-outcome-specificity, stakeholder-inclusion.
- **disqualifiers:** pure assertion without argument; is/ought conflation; values silently imported from WEIRD default.
- **weight_overrides:** empirical-strength x 0.3 (empirical scoring does not apply); coherence x 1.7.
- **fragility_flag:** fragile-normative (grounding is framework/practitioner + philosophical).

### INTERPRETIVE
- **load_bearing_properties:** credibility, plausibility, transferability, thickness, reflexivity-statement, data-adequacy, methodological-coherence.
- **disqualifiers:** no reflexivity statement; thin description presented as analysis; decontextualized quote mining; positivist-reliability metrics misapplied.
- **weight_overrides:** thickness x 1.4; sample-size x 0.5 (N is not the validity lever for interpretive).
- **fragility_flag:** none.
- **Source appraisal:** CERQual + Hammersley.

---

## Rules

1. The fragility flag is LOAD-BEARING -- it propagates to the Epistemic Output Layer as an explicit caveat. NEVER strip the flag for brevity.
2. Disqualifiers are binary (hit = reject the source); load-bearing properties are weighted.
3. Weight overrides multiply against the shared-dimension baseline defined in the Fitness Adjudicator's SHARED_SCHEMA.md. They never zero a dimension; the floor is 0.3.
4. Discipline priors (11 disciplines) contribute additional weight modifiers downstream -- S5 does NOT set them here; S7 composes them.
5. Admiralty input treatment is invariant: input-only.

## Anti-Patterns

- Emitting five independent rubrics: R2.4 forbids this. Parameterize the shared schema.
- Suppressing fragility flags because "the user already knows": the Output Layer depends on them; suppression silently miscalibrates confidence.
- Using Admiralty rating as the fitness judgment: R2.3 forbids this.
