Creating and Sustaining Safe and Inclusive Spaces for LGBTQ Youth: An Exploratory Investigation of the Role of Educational Professionals
Bibliographic record
Abstract
Recent evidence shows that lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ) youth regularly face hostile school environments. Those hostile environments contribute to making LGBTQ youth increasingly vulnerable to a number of emotional, behavioural, and social problems. Educators can play a critical role in buffering LGBTQ youth from potential victimization. As such, the present study explored the following questions: 1) What are the roles of educators (i.e., teachers, school administrators) with respect to promoting and creating safe and inclusive spaces for LGBTQ youth; 2) what unique contributions can educators make in nurturing those spaces; and, 3) what barriers do educators face in creating safe and inclusive spaces for LGBTQ youth? This study used a convergent parallel design mixed-methods approach. Fifty-six educators in Alberta completed an online survey; among those educators, 17 of them self-selected to participate in a semi-structured interview. Descriptive statistics were gathered from survey results; the interview data was analyzed using thematic analysis in order to generate themes relevant to the research questions. Those themes were: 1) Lack of awareness; 2) the use of inclusive language; 3) the role of inclusive curriculum in support of LGBTQ youth; 4) the role of educators as allies; 5) situational factors as barriers to supporting LGBTQ youth; and 6) supporting LGBTQ youth through GSAs. Overall, the results from the present study have future research implications and practical utility for educators and policy-makers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".