Is There a Transgender Canon?: Information Seeking and Use in the Transgender Community
Bibliographic record
Abstract
Because transgender issues are not often openly discussed, little is known about the transgender community and its information needs. Yet, people who are dealing with transgender issues may have pressing needs. In ongoing focus group research we are exploring the ways in which members of the community have sought to meet their own information needs.En raison du fait que les problèmes transgenres sont rarement abordés ouvertement, on connaît peu de choses sur la communauté transgenre et sur ses besoins informationnels. Cependant, les individus qui sont confrontés aux problèmes transgenres peuvent avoir des besoins pressants. À l’aide d’un groupe de discussion permanent, nous explorons les méthodes utilisées par les membres de cette communauté pour combler leurs propres besoins informationnels.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".