“We learn after freaking out”: teachers’ responses to transgender students in Brazilian high schools
Bibliographic record
Abstract
In Brazil, transgender/gender-diverse youth are initiating social transitions at an earlier age, reflecting more supportive settings and greater social awareness. Schools, as critical environments in which they spend most of their time, play a pivotal role in shaping their experiences. This case-study aims to provide insight into how Brazilian schools have been responding to the growing presence of transgender adolescents by examining the experiences of a group of teachers who, without prior education and training, encountered a transgender student for the first time. In-depth interviews were conducted with staff at one public high school located in Santos/Guarujá, Brazil. Data were analysed using reflexive thematic analysis within a constructivist paradigm. Our findings show how cisnormative assumptions fostered a trans-exclusionary culture, exposing transgender/gender-diverse students to misgendering, restricted access to facilities, and transphobic remarks. While coexistence between cisgender teachers and transgender students raised awareness among some school staff, it also generated exposure and embarrassment for the students. To foster more affirming environments, it is important to challenge the common sense about ‘equality’, which reinforces cisnormativity as an assumption for all students, and adopt policies to promote affirming change that does not rely solely on the presence of trans students.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".