Privacy for all students? Talking about and around trans students in “public”
Bibliographic record
Abstract
This paper places under examination the arguments used to fight against school policies and legislation intended to guarantee and protect the rights of trans students. That is, the paper's central investigation works to uncover the regimes of truth about children, gender, race and privacy implicit in the methods employed by activists who seek to counter the expansion of rights for trans students. Using critical discourse and document analyses influenced by queer theories and Critical Race Theory, this paper examines the group Privacy for All Students and the arguments it makes in campaign documents against California State Assembly Bill 1266 – the statewide trans students’ right law passed in 2013. First, this paper unpacks the intertwined constructions of children before moving to an examination of how notions of innocence are founded by gendered, sexual, and racial regimes of truth. Then, it explores how the foundational logics of the public sphere make possible for PFAS to address their arguments about children and innocence to “the public” and suggest why close, critical readings of the seemingly implicit ways of knowing and thinking about gender, race, and privacy in their documents are important towards ensuring trans students have a place in school.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.057 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| 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".