# DISCIPLINE_PRIORS -- Per-Discipline Weight Modifiers for the Parameterized Schema

**Provenance:** Phase 3 S02, R2.4. Petticrew & Roberts (2003) "Horses for Courses." Different disciplines privilege different load-bearing source properties; the literature does NOT support 11 independent rubrics, BUT does support per-discipline weight modifiers over the single schema.

**Hard rule:** Discipline priors MODIFY the weight vector ONLY. They NEVER:
- add a new dimension to D1-D5;
- remove a dimension from D1-D5;
- replace the claim-type profile's load-bearing dimension set;
- suppress the fragile-rubric flag.

All modifiers are bounded in [0.5, 1.5]. After applying, renormalize the weight vector so weights sum to 1.

---

## Modifier schema

```
discipline_priors[<discipline_tag>] = {
  D1_modifier: <float in [0.5, 1.5]>,
  D2_modifier: <float in [0.5, 1.5]>,
  D3_modifier: <float in [0.5, 1.5]>,
  D4_modifier: <float in [0.5, 1.5]>,
  D5_modifier: <float in [0.5, 1.5]>,
  privileged_properties: [...]           // informs D5 scoring, NOT new dimensions
}
```

The inherited 11 disciplines (Phase 2A): experimental-sciences, observational-epidemiology, econometrics, clinical-trials, ethnography, discourse-analytic, historiographic, realist-evaluation, practitioner-case, policy-analysis, indigenous-knowledge.

---

## Modifiers (tabular)

| Discipline | D1 | D2 | D3 | D4 | D5 | Privileged properties |
|---|---|---|---|---|---|---|
| experimental-sciences | 1.2 | 1.0 | 1.1 | 0.9 | 1.0 | preregistration, replication, effect-size stability |
| observational-epidemiology | 1.3 | 1.0 | 1.1 | 0.9 | 1.0 | confounding control, cohort characterization, RoB 2 domain signals |
| econometrics | 1.3 | 0.9 | 1.0 | 0.9 | 1.2 | identification strategy, robustness checks, placebo tests |
| clinical-trials | 1.2 | 1.1 | 1.1 | 0.9 | 1.0 | allocation concealment, blinding, ITT analysis |
| ethnography | 1.3 | 1.0 | 0.9 | 0.7 | 1.3 | thick description, reflexivity, sustained field presence |
| discourse-analytic | 1.2 | 1.0 | 0.9 | 0.7 | 1.3 | analytic transparency, corpus provenance, positionality |
| historiographic | 1.2 | 1.1 | 0.9 | 0.8 | 1.3 | primary-source provenance, chronological chain, archival integrity |
| realist-evaluation | 1.3 | 0.9 | 1.0 | 0.8 | 1.3 | CMO articulation, program-theory specification |
| practitioner-case | 1.1 | 0.9 | 1.0 | 0.9 | 1.1 | authorial accountability, outcome-traceability |
| policy-analysis | 1.1 | 1.0 | 1.2 | 0.9 | 1.1 | stakeholder transparency, evidence synthesis |
| indigenous-knowledge | 1.3 | 0.9 | 0.9 | 0.5 | 1.5 | relational accountability, custodianship, consent, place-based provenance (Chilisa) |

---

## Interpretation notes

- **D4 down-weighting for ethnography, discourse-analytic, indigenous-knowledge** (modifier < 1.0) reflects that Admiralty A-F was built for intelligence-reporting sources; it maps poorly to oral, relational, or place-based knowledge systems. The modifier expresses this -- it does NOT delete D4.
- **D5 up-weighting for indigenous-knowledge (1.5)** reflects that the discipline's privileged properties (relational accountability, custodianship, place-based provenance) are NOT captured by D1-D4 alone; D5 is where they enter.
- **D1 up-weighting across the table (>= 1.1)** reflects that across disciplines, inferential-load match is the primary fitness axis. The modifiers never drop D1 below 1.0.
- **Indigenous-knowledge D4 = 0.5** is at the modifier floor. This is load-bearing: Admiralty collapses hardest here (Baker 1968 applied to Western intelligence sources does not transfer). If this proves too tight or loose per Phase 9 evaluation, recalibrate via the reopen trigger in SKILL.md.

## Composition rule (called from SKILL.md Step 3)

1. Given a claim with `discipline_tags = [d1, d2, ...]`, look up each tag's modifiers.
2. If multiple tags are present, take the element-wise mean of the modifier vectors.
3. Multiply the claim-type profile's weight vector element-wise by the combined modifier vector.
4. Renormalize the result to sum to 1.
5. Pass to Step 4 (headline composition).

## What discipline priors NEVER do

- Override `load_bearing_dimensions` from CLAIM_TYPE_PROFILES.md.
- Suppress `fragile_rubric=true` on normative or predictive claims.
- Substitute for D1's claim-type-specific load-bearing property check. The D1 rationale still must reference one of the properties in the claim-type profile, regardless of discipline.
- Add a sixth dimension. The schema is fixed at D1-D5.
