--- name: annotator-input-parity-check description: | Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read. author: Claude Code version: 1.0.0 date: 2026-07-21 ---

# Annotator Input Parity Check

## Problem

A model built to replicate human labels is fed a different evidence base than the one the annotators used. The mismatch masquerades as a modeling or construct problem: recall collapses on the label subset whose evidence lives only in the annotators' source, audits produce increasingly sophisticated theory ("invisible" positives, construct splits, per-domain reliability gates), and successive model generations inherit the wrong input because each review critiques the lineage from inside the frozen input assumption.

## Context / Trigger Conditions

- Starting any label-replication build (classifier, LLM scorer, extractor) against hand-coded gold. - A validation report says some share of gold positives have "zero signal" in the model's input. - Proposals appear for: construct splits (what the model CAN see vs what the label encodes), human adjudication of "contested" cells, stop rules excluding weak domains, or accepting a permanent accuracy ceiling. - Verified instance (Specialist Directors US, 2026-07-21): three classifier generations (bio-BERT AUC 0.5 → structured RoBERTa "unclassifiable" on 3/5 domains → LLM dossier scorer with E/D construct split + PI adjudication + per-domain stop rules) all read director bios + BoardEx records, while the RA labels were pure transcriptions of PROXY-STATEMENT disclosures (skills matrices + bios, no exogenous data — confirmed in the source paper's methodology, 41 Yale J. Reg. 652, 669-72). The "invisible specialist" mass (43-79% of some domains) was simply the skills-matrix checkbox content the models were never shown. Years of downstream apparatus dissolved once the question "what did the labelers actually read?" was asked.

## Solution

1. Before any design work, write down the annotation protocol as the annotators executed it: source document(s), what they could see, what they could not, whether any exogenous data entered. Get this from the codebook/paper methodology section, not from folklore. If the protocol is unwritten, ask the PI directly: "did labelers consult anything beyond X?" 2. Compare against the model's planned input. Any evidence the annotators had that the model lacks is a hard recall ceiling on exactly the labels that evidence determines — no architecture, prompt, or training fixes it. 3. If a mismatch exists, prefer restoring input parity (give the model the annotators' document) over modeling around the gap. For transcription-style protocols, the task then becomes extraction, not prediction, and validation against the hand labels becomes construct-matched (agreement should be high; disagreement means extraction bugs, not construct philosophy). 4. Only if input parity is impossible (annotators used private knowledge, interviews, paywalled data) is a construct split the honest design — and then the model's output must be named as a DIFFERENT variable, never graded raw against the full gold. 5. When auditing an EXISTING lineage: ask the parity question first, before critiquing rubrics, thresholds, or gold quality. An audit that inherits the input assumption can be internally excellent and still miss the dominant error term.

## Verification

- The protocol-input inventory exists in writing and the model input is a superset of it → recall ceilings from "invisible" labels should disappear; residual disagreement decomposes into extraction errors (fixable) rather than unknowable-label mass. - Quick falsification test for a claimed "unpredictable" label subset: pull 5 such gold positives, open the annotators' source document for each, and check whether the label is visible there. If yes, the problem is input, not construct.

## Notes

- Distinct from [llm-gold-bound-failure-check], which diagnoses gold that fails to SEPARATE classes for a proposed revision; this skill diagnoses model INPUT that omits the annotators' evidence. Run this parity check first — gold-bound analysis of a parity-broken system wastes effort. - The mismatch is self-perpetuating across model generations: each successor inherits the predecessor's feature pipeline, and each audit optimizes within it. Breaking the frame requires asking about the ANNOTATORS, not the model. - Construct splits built on a parity-broken system may still have salvage value for a different question (e.g. record-evident-but-undisclosed expertise is analytically interesting in its own right) — reframe, don't necessarily discard.