Part IV

Methods and Replication

How the dataset was built and checked, and how to re-derive every registered number yourself.

The dataset behind the Note’s Part II did not exist before this project. Court opinions were retrieved, screened by a frozen prompt, and classified by several separately run AI models from different providers, with disagreements resolved under prespecified adjudication rules; the headline finding has its own separate ensemble, audited blind by a different vendor’s models. The models classify — answering fixed questions about each opinion; the author directed the project and made all legal and interpretive judgments. Validation measures reproducibility across independent classifiers, not accuracy against a human-coded benchmark, and every layer is published with its instruments.

The method, documented

Reproduce it

One command runs the deterministic release gate — no network access, API keys, or spend — and re-derives the registered series from the committed record:

python scripts/run_release_checks.py

Where replication stops

Full case texts are not redistributed; source identifiers and hashes are preserved so each text can be re-obtained. End-to-end corpus reconstruction requires upstream working files retained privately; the frozen canonical record published in the repository is the replication baseline. The evidence and limits page states what remains judgment rather than mechanics.