“Our Metadata, Ourselves”: The Trans Metadata Collective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it