# CLAIM_TYPE_PROFILES -- Per-Claim-Type Weight Vectors, Load-Bearing Dimensions, Disqualifier Hooks, Fragile-Rubric Markers

**Provenance:** Phase 3 S02 -- GRADE / RoB 2 / ROBINS-I (causal); Brier / Good Judgment adaptation (predictive); AACODS + Petticrew (descriptive); CERQual + Hammersley (interpretive); Dorst + realist evaluation (normative). Phase 3 Tier-1 adjudication mandated these live as weight-vector parameterizations of the single schema, NOT as five parallel rubrics.

**Hard rule:** A profile changes weights, names load-bearing dimensions, hooks disqualifiers, and sets the fragile-rubric marker. It NEVER invents a new dimension. If a profile adds or subtracts from D1-D5, the architecture has broken -- stop and repair.

---

## Profile schema

```
profile_<claim_type> = {
  weight_vector: {D1: w, D2: w, D3: w, D4: w, D5: w},   // sum to 1 before discipline priors
  load_bearing_dimensions: [Di, Dj, ...],                // score 0-1 on any of these => disqualifier path
  claim_type_specific_load_bearing_properties: [...],    // named properties the D1 rationale MUST reference
  disqualifier_hook_ids: [...],                          // DISQUALIFIERS.md identifiers
  fragile_rubric: <bool>,                                // TRUE for normative and predictive ONLY
  notes: "..."
}
```

---

## profile_descriptive

- `weight_vector: {D1: 0.30, D2: 0.25, D3: 0.15, D4: 0.20, D5: 0.10}`
- `load_bearing_dimensions: [D1, D2]` -- provenance and integrity are non-substitutable for descriptive claims.
- `claim_type_specific_load_bearing_properties: ["provenance_chain_traceable", "authority_of_source", "date_currency_vs_claim"]` -- AACODS operationalization (Tyndall 2010; Petticrew & Roberts 2003).
- `disqualifier_hook_ids: ["AACODS_FLOOR"]`
- `fragile_rubric: false`
- Notes: D4 Admiralty matters but never substitutes for a missing provenance chain. RCTs are typically overkill for descriptive claims -- do NOT weight study design over provenance.

## profile_causal

- `weight_vector: {D1: 0.40, D2: 0.20, D3: 0.10, D4: 0.15, D5: 0.15}`
- `load_bearing_dimensions: [D1, D5]` -- internal validity AND discipline-appropriate identification strategy.
- `claim_type_specific_load_bearing_properties: ["internal_validity_domain_signals_from_RoB2", "identification_strategy_explicit", "GRADE_certainty_rating_per_outcome"]`.
- `disqualifier_hook_ids: ["CAUSAL_IDENTIFICATION_MISSING"]`
- `fragile_rubric: false`
- Notes: GRADE's per-outcome certainty is the D1 anchor. A source can be A1 Admiralty yet score D1=1 if confounding is unaddressed. Do NOT let D4 pull the composite up when D1 is weak.

## profile_predictive

- `weight_vector: {D1: 0.45, D2: 0.15, D3: 0.10, D4: 0.15, D5: 0.15}`
- `load_bearing_dimensions: [D1]` -- out-of-sample calibration is non-substitutable.
- `claim_type_specific_load_bearing_properties: ["out_of_sample_track_record", "Brier_or_calibration_trace", "forecaster_accountability"]`.
- `disqualifier_hook_ids: ["PREDICTIVE_CALIBRATION_MISSING"]`
- `fragile_rubric: TRUE` -- no GRADE-equivalent exists for predictive-source appraisal; the plugin adapts forecaster Brier / Good Judgment logic. ALL predictive scoring runs emit `fragile_rubric=true`.
- Notes: A peer-reviewed theoretical model with no out-of-sample validation scores LOWER on D1 than a blog by a forecaster with a documented Brier score. This inverts intuitive source hierarchies -- that is intentional and literature-backed (Tetlock & Mellers).

## profile_interpretive

- `weight_vector: {D1: 0.35, D2: 0.20, D3: 0.15, D4: 0.10, D5: 0.20}`
- `load_bearing_dimensions: [D1, D5]` -- thickness/reflexivity AND discipline alignment.
- `claim_type_specific_load_bearing_properties: ["thick_description_present", "reflexivity_statement_present", "CERQual_coherence_and_adequacy", "transferability_discussion"]`.
- `disqualifier_hook_ids: ["INTERPRETIVE_REFLEXIVITY_MISSING"]`
- `fragile_rubric: false`
- Notes: Positivist reliability metrics do NOT apply. Hammersley rejects them; CERQual reframes them. If the D1 rationale uses words like "reliability" or "inter-rater agreement" as the primary load, the rationale is wrong for claim type -- repair via ANTI_DEVIATION.md.

## profile_normative

- `weight_vector: {D1: 0.50, D2: 0.10, D3: 0.10, D4: 0.10, D5: 0.20}`
- `load_bearing_dimensions: [D1]` -- argumentative coherence is non-substitutable.
- `claim_type_specific_load_bearing_properties: ["argumentative_coherence_trace", "reflective_equilibrium_signals", "authorial_accountability_for_prescription", "CMO_structure_for_ought_claims_where_applicable"]`.
- `disqualifier_hook_ids: ["NORMATIVE_COHERENCE_MISSING"]`
- `fragile_rubric: TRUE` -- the empirical grounding is weakest here; Dorst is framework/practitioner and metaethics is philosophical. ALL normative scoring runs emit `fragile_rubric=true`.
- Notes: Do NOT default to empirical-weight scoring. A source can be A1 Admiralty, have internal validity, and still fail D1 for normative claims if the argumentative chain is missing. Fragile-rubric defaulting is a top deviation failure (ANTI_DEVIATION.md Check 5).

---

## Application order (called from SKILL.md Step 3)

1. Look up profile by claim_type.
2. Apply DISCIPLINE_PRIORS.md modifiers to the weight_vector; renormalize.
3. Consult load_bearing_dimensions -- any score <= 1 on a load-bearing dimension triggers DISQUALIFIERS.md path.
4. Set fragile_rubric flag from profile. Do not suppress.
5. Confirm the D1 rationale references at least one claim_type_specific_load_bearing_property. If not, the rationale is off-type -- return to Step 2 with instruction to rescore D1 against the correct property set.
