“Pray That God Will Change You”: The Religious Social Ecology of Bias-Based Bullying Targeting Sexual and Gender Minority Youth—A Qualitative Study of Service Providers and Educators
Bibliographic record
Abstract
The bullying of sexual and gender minority youth (SGMY) is pervasive, with documented negative impacts on health. We explored the social ecology of bullying of SGMY, with a focus on religion as a source or context of bullying. Semistructured interviews with service providers, educators, and administrators in Toronto, Canada, who work with SGMY explored perspectives on the bullying of SGMY, focusing on religiously based bullying and strategies for intervention. Interviews (45-60 minutes) were recorded, transcribed, and analyzed using thematic content analysis. The data revealed religiously based homophobic discourse that permeates religious (places of worship, faith-based schools) and secular microsystems (public schools, families) across SGMY’s social ecology. The language and ideology of “sin” and “conversion” were evidenced in direct religiously based bullying of SGMY in schools, and victimization in places of worship and family microsystems, as well as serving as a rationale for bullying and nonintervention by teachers, school staff, administrators, and family members. Multisectoral and multilevel influences of religiously based sexual prejudice on the bullying of SGMY suggest that existing individual-level and microsystem-level responses in schools should be augmented with institutional, policy, and legal interventions in SGMY’s more distal social ecology in order to effectively prevent religiously based homophobic bullying.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".