Metadata Best Practices for Trans and Gender Diverse Resources
Bibliographic record
Abstract
This document is the result of a year of work and collaboration by the Trans Metadata Collective (TMDC; https://transmetadatacollective.org/), a group of dozens of cataloguers, librarians, archivists, scholars, and information professionals with a concerted interest in improving the description and classification of trans and gender diverse people in GLAMS (Galleries, Libraries, Archives, Museums and Special Collections). The Collective’s primary goal was to develop a set of best practices for the description, cataloguing, and classification of information resources as well as the creation of metadata about trans and gender diverse people, including authors and other creators
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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 | 0.082 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.031 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.032 | 0.042 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".