Learning without subjects?: Considering trans pedagogies in practice
Bibliographic record
Abstract
Historically, state-regulated compulsory schooling has been a staging ground for the subjection of learners into categories of differential worth: race, gender, intellectual ability, class, and beyond. Yet, what might it look like to consider learning without subjection and subjugation? Here, Keenan draws from trans studies to consider how trans pedagogies contribute to the project of envisioning education beyond subjection. Presenting data from 11 one-on-one interviews with educators and artists from within or close to trans communities whose work takes place primarily beyond schools, Keenan argues that trans pedagogies resist the creation of a calcified subject, "sliding away from deteriminancy" (Aizura et al., p. 128) by emphasizing dialogic and relational approaches to learning, artistic expression, and attention to the carceral structures that govern gender in the Global North.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.052 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| 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".