Broken Promises: Welcoming LGBTQ+ Teachers into the Profession
Bibliographic record
Abstract
Despite advances in rights for the LGBTQ communities, LGBTQ teachers continue to face on-going and systematic discrimination in the workplace. This contradiction is devastating for LGBTQ teacher candidates. LGBTQ teacher candidates often enter education programs because schools have been important sites of learning about their sexual and gender identities. These students came out in school, organized GSAs, took their same-sex partners to prom, and even if school wasn’t always welcoming, they felt a commitment to making schooling more inclusive. And so, they decide to become teachers themselves. When they meet the professional teacher education program, they are shocked to face questions about the relationship between their sexuality, gender and teaching identities. Should they come out to their practicum supervisors? Does their dress and gendered presentation of self reflect the professional norms of teaching? And for teacher candidates who may themselves transition genders during their practicum, how can Faculties of Education both support their professional development and create new spaces for their emergent gender identities? These questions point to productive and troubling tensions. The papers in this panel explore how teachers, and teacher educators, understand, navigate, reconcile, and resist the relationship between the idea of “the teacher” and LGBTQ+ identities.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".