# Probe 2 -- Covert Tier-Bias

**Probe ID:** BSP-02-COVERT-TIER-BIAS
**Probe version:** 1.0
**Position in fixed probe order:** 2 of 6

---

## Definition

Covert tier-bias is the silent mislabeling of source quality through tier taxonomies that look objective but mask underlying frame alignment. It differs from monoculture: monoculture is one source carrying the claim; covert tier-bias is many sources carrying the claim but all granted inflated tier status because they align with the dominant frame. The failure is in the labeling apparatus, not the concentration.

The canonical pattern: a non-peer-reviewed working paper is labeled "Tier 1" because it aligns with the analyst's prior; a peer-reviewed contradicting study is labeled "Tier 2" on grounds that would not apply if frame were reversed. Same labels, opposite assignments under frame rotation.

## Trigger conditions

Fire on any of:

- Inconsistent tier application -- the same source class (e.g., working papers from the same venue) receives different tier labels across the claim set without documented criterion shift.
- Tier masking quality -- a source labeled "Tier 1" fails an independent tier criterion (peer review, replication record, venue impact) that another same-labeled source passes.
- Tier inflation on same-frame sources -- sources aligned with the RAM frame receive systematically higher tier labels than counter-frame sources after controlling for venue and method. Threshold: mean-tier gap >= 1 tier level between aligned and counter-aligned subsets.
- Tier opacity -- tier labels applied without documented rubric; labels unreviewable.

Severity grading: inconsistent-application -> medium; masking-quality on <=2 sources -> medium; systematic inflation (mean gap >= 1) -> high; tier opacity on load-bearing claim -> critical.

## Method

1. Extract tier labels per source from the Audit Method Engine artifact.
2. Consult `evaluator/references/audit-doctrine/FRAME_CRITERION_CATALOG.md` for the independent tier criterion applicable to the loaded frame.
3. Recompute a frame-independent tier score per source using the catalog's objective signals (peer review, retraction record, citation-age curve, venue rank).
4. Compare RAM-assigned tier vs recomputed tier per source. Tabulate discrepancies.
5. Test for frame alignment: cluster sources by alignment-with-RAM-conclusion; compute mean-tier gap between aligned and counter-aligned subsets.
6. Record all discrepancies; do NOT average them away.

## Evidence requirement

- `tier_assignments_ram` -- full list, source -> tier.
- `tier_assignments_independent` -- recomputed list.
- `discrepancies` -- sources where RAM-tier and independent-tier differ by >=1 level.
- `alignment_tier_gap` -- mean-tier gap between frame-aligned and counter-aligned subsets; include n per subset.
- `rubric_documented` -- boolean; whether RAM's tier rubric was present in the artifact.

## Output record schema

```
{
  "probe_id": "BSP-02-COVERT-TIER-BIAS",
  "probe_version": "1.0",
  "fired": true|false,
  "severity": "low"|"medium"|"high"|"critical",
  "claim_id": <string>,
  "run_id": <string>,
  "discrepancy_count": <int>,
  "alignment_tier_gap": <float>,
  "evidence": { ... as above ... },
  "taxonomy_entry_candidate": <taxonomy entry id or "new">
}
```

## Anti-examples

- Do NOT fire on a single discrepancy without frame-alignment analysis; one-off mistier is noise, systematic mistier is the defect.
- Do NOT accept RAM's tier rubric on face; independent recomputation is mandatory.
- Do NOT collapse the "unknown alignment" cluster into "aligned" or "counter-aligned"; keep it separate to avoid forcing the signal.
