Gentle Introduction to AI for Digital Humanities: taught stylometric classification

Research applicability

Inspected Week2 goes beyond a course outline: classification, nearest-neighbour authorship attribution, word-frequency features and multivariate stylometry establish a bounded taught humanities analysis task.

Getting started

Untested editorial suggestion: read the retained primary evidence and limitations; researchers must verify source support before use.

Maintenance

Dated source snapshot only; present availability, maintenance and version parity require separate checks.

License

Source visibility is not reuse clearance; source/model/data licenses and rights require separate checking.

Workflow & human oversight

Scope: Documentation-only; no repository code executed, outcomes reproduced, private inputs fetched or production decisions changed. Human control: Editorial inference from the taught task: a researcher chooses corpus/features and interprets comparisons. Source does not demonstrate a formal output-review gate. Stage connections: - Choose a comparison corpus and text features - Use classification/nearest-neighbour analysis as taught - Interpret stylistic comparisons in the authorship question Stopping: No additional stopping policy established by this representation. Missing evidence: No autonomous agent, confirmed disputed authorship, model accuracy or executed exercise established. Not all clustering is labelled AI.

Workflow level is not an evidence score; higher is not better evidence.

Approach

- Choose a comparison corpus and text features - Use classification/nearest-neighbour analysis as taught - Interpret stylistic comparisons in the authorship question

Outcomes (as reported by the originating team)

Documented method or intended capability; no empirical research benefit established by this review.

Limitations

- Partial course review: README and actual Week2 slides only. - Week1 response exceeded fixed response-size cap; no retry/cap increase. - No code or slides JavaScript executed. - L1 is an editorial classification of the specific taught ML-assisted task, not a measured implementation claim.

Reproducibility

AI editorial source review only. No candidate code, model, agent, experiment or reported outcome independently executed or reproduced. Documentation-only; no repository code executed, outcomes reproduced, private inputs fetched or production decisions changed.

Adoption requirements

Untested editorial suggestion: define a bounded lawful task, inspect rights/model/data requirements and verify results before use.