# Human-labeled evaluation data — schema

All NeurIPS-track evaluations (zero-shot, few-shot, contrastive) read from
this directory. **Synthetic cross-skill pairs are training-only.** The
files in this directory are the ground truth.

## Files

```
neurips_submission/data/labeled/
├── eval.parquet               ← REQUIRED — full labeled evaluation set
├── eval_dev.parquet           ← OPTIONAL — small dev subset for threshold tuning
└── label_provenance.json      ← OPTIONAL — labeler IDs, dates, agreement stats
```

`eval.parquet` is what every evaluator script consumes by default. If you
choose to split into dev/test for threshold tuning, the eval scripts can be
pointed at either via `--pairs-eval`.

## Required columns

| column | type | description |
|---|---|---|
| `pair_id` | string | Stable unique ID per labeled item. Recommend `sha1(skill_id + label_source)`. |
| `anchor_skill_id` | string | The skill being evaluated. Matches `skill_id` in `normalized/v1/normalized_skill_records.parquet`. |
| `description` | string | The skill's description text (METADATA inner content; ≤ ~2K chars recommended). |
| `code` | string | The skill's code/resource text (RESOURCE inner content, joined files; ≤ ~4K chars recommended). |
| `label` | string | One of `"aligned"` / `"misaligned"` (binary task). |
| `label_fine` | string | OPTIONAL fine-grained: `"aligned"`, `"benign_misaligned"`, `"malicious_misaligned"`, `"ambiguous"`. Maps to binary at eval time. |

## Optional columns (recommended for paper analysis)

| column | type | description |
|---|---|---|
| `labeler_id` | string | Anonymized labeler identifier. |
| `labeler_confidence` | float | 0–1 self-rated confidence. |
| `n_labelers` | int | How many labelers saw this item. |
| `agreement` | float | Inter-annotator agreement on this item (e.g. 1.0 if all agree). |
| `notes` | string | Freeform labeler comment. |
| `source_skill_md_path` | string | Path to the original SKILL.md, for traceability. |

## Label semantics (binary task)

Map fine-grained → binary as follows when `label_fine` is present:

| `label_fine` | binary `label` | rationale |
|---|---|---|
| `aligned` | `aligned` | code does what description says |
| `benign_misaligned` | `misaligned` | description ≠ behavior, but no harm |
| `malicious_misaligned` | `misaligned` | description hides harmful behavior |
| `ambiguous` | EXCLUDE from eval | low-confidence; not a clear positive or negative |

`ambiguous` rows should be dropped before scoring or kept in a separate
`ambiguous.parquet` for qualitative analysis.

## Size & balance expectations

- **Total**: a few thousand items (per the user's plan: low-thousands range).
- **Class balance**: aim for 50/50 aligned/misaligned where feasible. If
  natural prevalence is skewed, oversample misaligned for the labeled set.
- **Coverage**: include skills from both the `cpt` and `unseen` pools so
  the NeurIPS eval is independent of the MISQ-track CPT training corpus.

## Construction notes

- **Source candidates** for labeling can be drawn from
  `normalized/v1/normalized_skill_records.parquet` or
  `full_cpt/full_cpt_v2/stage2/{train,val,test,unseen}.parquet`.
- A reasonable starter sampling scheme:
  - Stratify by skill domain (developer-tools / ML / data / web / etc).
  - Sample 50% from `unseen` pool and 50% from `cpt` pool so both NeurIPS
    eval and MISQ-track CPT analysis can use the same labels (with a clear
    cpt-overlap caveat noted in the paper).
- The same item can appear under multiple labelers; use `pair_id =
  sha1(skill_id + labeler_id)` if you want per-labeler rows, then aggregate
  by majority vote into a single `eval.parquet` row.

## Until labels exist

Synthetic cross-skill pairs in
`neurips_submission/data/pairs_test_synthetic.parquet` and
`pairs_val_synthetic.parquet` are smoke-test placeholders **only**. Eval
scripts will fall back to them with a loud warning if `labeled/eval.parquet`
is missing — this is for pipeline validation, not for paper numbers.