Bibliographic record
Abstract
In recent years, there has been an increasing awareness of transgender culture, issues, and experiences. In popular culture, trans celebrities such as Laverne Cox, Chaz Bono, and Janet Mock have been a part of this shift, often acting as celebrity spokespeople to increase understanding of trans issues. Even with the greater visibility of trans lives in popular culture, ongoing court battles like G.G. v. Gloucester County School Board (a US case centered on trans students’ rights to use communal bathrooms congruent with their gender) demonstrate the need for greater understanding and acceptance.As co-authors, we have had the privilege of working with materials on loan from the Transgender Archives at the University of Victoria (Canada), the largest transgender archive in the world. This experience, which included collecting comments from library patrons who viewed the collection materials, highlighted for us the role that libraries and archives play in laying the groundwork for increased diversity, awareness, and inclusion related to trans lives, culture, and community. It is not only a matter of meeting the information needs of those who are coming out as transgender, but the wider community of family (spouses, children, parents, etc.), friends, and allies. And, alongside the value of providing information with direct practical application, patrons’ comments underscored how the inclusion of trans resources at the library enriches our cultural imaginary, and creates the space for imagining and living what they have sometimes felt to be “impossible lives.”
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 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".