Building LGBTQ awareness and allies in our teacher education community and beyond
Bibliographic record
Abstract
In this article we share the impact of a training program (Positive Space I and Positive Space II) on pre-service teachers’ understandings of and abilities to create safe spaces for Lesbian, Gay, Bi-sexual, Transgendered and Queering/Questioning (LGBTQ) youth and allies in our teacher-education program and in schools. Research has demonstrated LGBTQ youth are more likely to feel unsafe, alienated and more vulnerable than their heterosexual counterparts in schools and society. Our discussion focuses upon the impact of this training program, and considers challenges and best practices to build awareness and allies in our own higher-education context, as well as to help create better learning communities for LGBTQ youth and allies in schools. We suggest this particular program is an example of how to work towards the development of a pedagogy that does not oppress; one that truly embraces, celebrates, and honours all learners.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".