Introduction: Unmasking Transphobia, Building Transpositive Solidarities
Bibliographic record
Abstract
The historical catalyst for this collection of essays is a tragic and disturbing one, consisting both of a particular event and a general global pattern. In March of 2023, Joanne Boucher, a faculty member in the Department of Political Science at the University of Winnipeg, delivered a public talk with the dodgy title, “The Commodification of the Human Body: The Case of Transgender Identities.” According to the event description, Boucher’s talk was to explore the “economic interests involved in transgenderism” and to investigate the intersection of “government, corporate-funded lobby groups, the medical industry and the biotechnology sector.” Although framed in neutral-sounding academic jargon, both the event title and the event description contained blaring red flags, readily identifiable even to casual readers. Far from being a unique and isolated event, Boucher’s talk was part of a much larger and more general global explosion of transphobic discourse, which has been expressed in recent years in the form of utterly cruel and inhuman legislation. In response to this frightening national and global drift, the University of Winnipeg’s Centre for Research in Cultural Studies (CRiCS) organized a public event aimed at understanding our current political moment and offering guidance for solidarity and praxis. This collection features essay versions of the informal talks delivered at the March 2023 event.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".