Supporting Transgender Students and Gender-Expansive Education in Schools: Investigating Policy, Pedagogy, and Curricular Implications
Bibliographic record
Abstract
Context/Background: This article provides an introduction to the special issue. It includes an overview of a collection of articles from scholars across the globe who are committed to deepening an understanding of the experiences of trans students and gender-expansive education in schools. The special issue grew out of concerns about the need to investigate a trans studies–informed approach to addressing trans marginalization that attends to questions of both gender and racial justice in K-12 schools—an approach that is much needed in the field. The special issue also emerges, and needs to be contextualized, in response to the current conditions of resurgent far-right extremism, with its accompanying anti-trans and white supremacist rhetoric. Purpose: The purpose of this article is to provide both an introduction to the special issue and a rationale for its conception. It serves as an orientation to reading of the special issue as a whole, functioning as a synthesizing introduction: a point of reference and lens through which to situate the contributing articles in a dialogic relation to mark a distinctive assemblage in the field both within and beyond the North American context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| 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".