Creating Inclusive Schools for LGBTQ Populations - A Study Exploring Strategies School Leaders Employ for LGBTQ Inclusion
Bibliographic record
Abstract
The purpose of this study is to explore specific strategies that inclusive-minded elementary principals use to create more inclusive school environments for LGBTQ populations, which continue to be underrepresented in different facets of school community life. Qualitative interview data was collected across different school districts from thirteen elementary principals who actively promote the inclusion of LGBTQ populations in Ontario schools. The data suggests that, despite the number of barriers that principals face in their efforts to create more inclusive school environments for LGBTQ populations, school leaders employ many strategies to strategically and intentionally facilitate school environments that are welcoming, respectful, and inclusive for individuals who identify as LGBTQ. The findings of this research have implications for the urgency for this work as well as the possibilities to realize school communities which are inclusive of LGBTQ communities. The findings also have implications for professional learning for leaders in the area of inclusion and social justice. In addition, supports and appropriate resources that are needed to enhance the work of LGBTQ inclusion in schools are highlighted.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".