Supporting Transgender Inclusion and Gender Diversity in Schools: A Critical Policy Analysis
Bibliographic record
Abstract
In this article, we conduct a policy analysis of transgender affirmative policies in Ontario and examine their implications for addressing gender justice and gender democratization in the school system. By adopting a case study approach, we provide a critical analysis of these policies and of how stakeholders with familiarity and knowledge of trans-affirmative policies from two school boards in Ontario are making sense of their impact with respect to addressing trans inclusion in schools. As such, our study offers insight into two trans-affirmative policies and their implications for both supporting transgender, gender non-conforming and non-binary students and envisioning gender-expansive education in the school system. We draw on interviews with key informants-two teachers and a school board official-as a basis for reflecting on the need to move beyond a discourse of accommodation in trans inclusive policies to one that explicitly articulates a pedagogical commitment to gender justice and gender democratization in schools.
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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 | 0.056 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.036 | 0.063 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".