validation/human_labeling/README.md

Human labelling kit

Every validation figure for PyPI API Drift so far comes from AI agents. This kit lets two people check a sample by hand, so the dataset's precision and the AI auditor's reliability can be measured independently.

What is in it

File	Purpose
items.csv	200 items: the 160 breaking-change claims of the v1.2 precision audit (C…, an unbiased random sample, so the human precision is directly comparable with the AI audit's 91.9%) and 40 random VersionProbe pairs from the 400-pair benchmark audit (P…)
labels_A.csv, labels_B.csv	blank sheets, one per labeller
check_item.py	prints whether a dotted path exists in the current environment, and its signature
ai_key_DO_NOT_OPEN_BEFORE_LABELLING.csv	the AI auditor's verdicts; open only after both sheets are complete
score_agreement.py	Cohen's kappa between labellers and with the AI, human precision with a 95% CI, items needing adjudication

Instructions for labellers

1. Work independently. Do not discuss items, and do not open the AI key.
2. For each row of items.csv, decide whether the claim is right:
   • correct: code written for the old release that relies on the named API fails on the new release (for a benchmark pair: the snippet works on the old release and fails on the new one because of the named API).
   • incorrect: the old code keeps working on the new release, or it never worked on the old one, or the failure has a different cause.
   • unsure: you could not decide in reasonable time (leave a note).
3. Evidence may come from running the code (how_to_check gives a recipe using check_item.py), release notes, changelogs or documentation. Write one line of evidence for every verdict.
4. A name counts as public unless it, or a module on its path, starts with an underscore. A deprecation warning is not a break; only failure is.
5. Record roughly how many minutes the item took. Expect 2–5 minutes per item with a fast package installer such as uv.

After labelling

python score_agreement.py --json results.json

The script lists items where the two labellers disagree or one was unsure. A third person decides those in labels_adjudicated.csv (same columns), then re-run. Report: kappa between labellers, kappa of each with the AI, human precision for claims and for benchmark pairs, and the items on which the AI was wrong.