Independent review

pyaegean includes a one-command review path for people who want to check the public evidence before reading the rest of the repository:

git clone https://github.com/ryanpavlicek/pyaegean.git
cd pyaegean
python scripts/reproduce_review.py

The command runs on CPython 3.10 through 3.14. It uses the standard library and the checked-out zero-dependency source. It verifies a canonical SHA-256 manifest whose records each choose byte-exact or cross-platform canonical-LF hashing, runs the project-authored offline regression fixture and one baseline Greek sentence, and compares the result byte-for-byte with the reviewed expectation. When Git is available, it also reports the commit and whether the checkout is clean.

It does not download or run the neural model, use the network, write bytecode, or create a pyaegean cache. The fixture result is not a neural accuracy claim: reproducing the published neural rows needs the complete pinned protocol on the Benchmarks page.

flowchart TD
    A["Clean source checkout"] --> B["Verify manifest and record hashes"]
    A --> C["Run zero-dependency fixture"]
    B --> D["Receipt with exact digests"]
    C --> D
    D --> E["Inspect the model, data, claims, and limitations"]
    E --> F["Report a match or discrepancy"]

Reading the receipt

A pass establishes two things:

1. Every public record in the review manifest has the reviewed bytes.
2. The deterministic offline journey gives the reviewed result on this interpreter.

A pass does not certify the scholarly interpretation of a source, rerun a large model, or amount to outside peer review. --json prints the complete machine-readable receipt. --allow-dirty is available for local diagnosis, but a dirty run is not clean reproduction evidence.

What to inspect next

The repository's independent review kit contains the canonical manifest, a model card, a data card, an evidence map, a limitations matrix, and the discrepancy-report path. The measured-value registry is training/results/published-claims.json, and this site's Methodology page explains the data, architecture, evaluation, and review rules in full.

The model card identifies the shipped artifact and its intended use. The data card describes training and evaluation roles, separation, licenses, and coverage limits. The wiki's Validation and review page states what has and has not had outside review, and Limitations keeps the full boundary register.

Reporting a mismatch

Open an independent-review discrepancy with the exact command, package version, Git commit or source-archive hash, manifest SHA-256, deterministic-result SHA-256, environment, observed output, and all local modifications. Record a discrepancy exactly; it is useful evidence.

Prospective maintainers can start with the ownership slices in CONTRIBUTING.md.