“Our Metadata, Ourselves”: The Trans Metadata Collective
Bibliographic record
Abstract
ABSTRACT This paper presents the history, internal processes, and finalized report of the Trans Metadata Collective (TMDC), founded to address the lack of attention paid to trans and gender diverse issues in galleries, archives, libraries, museums, and special collections (GLAMS). The TMDC, an ad‐hoc group of nearly a hundred information professionals, developed best practices for the description and classification of trans and gender diverse information resources. These guidelines prioritize transparency, cultural sensitivity, correct identification, explicit descriptions of transphobia, and regular assessment of trans‐related content. It examines the effects of commonly used standards and controlled vocabularies such as Resource Description and Access (RDA) and Library of Congress Subject Headings (LCSH) on trans and gender diverse people and critiques the inadequacy of these standards' representation of those communities. The TMDC provides guidance for using existing LCSHs, recommends alternative subject vocabularies, and proposes revisions to improve representation. The paper advocates individual agency in naming and gender identification, with recommendations on contacting creators and documenting their preferences. The TMDC emphasizes the importance of minimizing potential harm and protecting privacy in metadata creation. Overall, the report aims to enhance the representation and inclusion of trans and gender diverse communities in GLAMS institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".