GitHub, Colab and Drive

How the reproducible sense-and-function workflow is organised.

What lives where

LocationContents
GitHubScripts, configuration, taxonomies, templates, prompts, documentation and small examples.
Google Drive or institutional storageRaw corpora, processed data, Pass 1 outputs, informed Pass 2 outputs, blind-validation outputs, adjudication files, logs and review workbooks.
Colab or local PythonExecution environment for the reproducible pipeline.

Pass 2 inputs

The production Pass 2 scripts require both Pass 1 annotation files:

This enables informed critical review. The --blind option hides both Pass 1 outputs for a separate validation sample.

Keep blind and informed outputs separate

outputs/en/sense_pass2_informed.csv
outputs/en/function_pass2_informed.csv
outputs/en/sense_pass2_blind.csv
outputs/en/function_pass2_blind.csv

Do not append blind and informed rows to the same CSV, because the review condition is part of the provenance.

Required identifiers

All files must preserve the stable row_id. This is what allows sampled sentences, sense annotations, function annotations and human review decisions to be joined safely.

Secrets and large data

API keys and large corpus files must not be committed to GitHub. Keep secrets in environment variables or protected storage and preserve corpus licensing conditions.