Revising sex and gender in the TEI Guidelines
Bibliographic record
Abstract
With the October 2022 release of the TEI Guidelines, the TEI Technical Council introduced a element and several revisions to the documentation of related elements and attributes. These revisions respond to calls in the TEI community for a way to encode gender in the context of prosopography (or TEI personography), distinct from linguistic morphological gender. In the process of introducing the new encoding, the Council revised passages of the Names, Dates, People, and Places (ND) chapter to remove prescriptive statements about sex and gender, and to modify the TEI’s guidance on representing individual states and traits. Building on the authors’ presentation to the September 2022 TEI Conference, this article discusses the 2022 efforts as one stage in a series of revisions over the past decade, provides background on the theory guiding their work, explores standoff personography applications of the new elements and attributes to a passage of Virginia Woolf’s Orlando, and discusses an experimental encoding of sex and gender in cast lists from early modern playbooks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".