Queering Shelter: Homelessness Service Provider Perspectives on Programming for 2SLGBTQ+ Adults in Montreal, Canada
Bibliographic record
Abstract
2SLGBTQ+ adults face unique experiences of homelessness often marked by heightened victimization. Notably, homelessness resources represent challenging environments where, in addition to risks of discrimination, 2SLGBTQ+ adults often do not have support that reflects their unique needs to exit homelessness. The case of Montreal, Canada, is particularly concerning as no resource specifically supports this group. This research set out with the following questions: 1. How are homelessness organizations in Montreal currently responding to the needs of 2SLGBTQ+ community members? 2. How do workers in housing and homelessness organizations imagine shifts to better support the 2SLGBTQ+ community? 3. What policies, practices, and training would support these shifts? Correspondingly, we facilitated three focus groups with a total of 15 individuals currently working in one Montreal homelessness resource. Findings revealed the following themes and recommendations: unique experiences and corresponding needs of 2SLGBTQ+ adults; homelessness resources and supports for 2SLGBTQ+ people in Montreal; staff hold competing views on the effectiveness of segregated or integrated supports for 2SLGBTQ+ people; and that information and training for staff requires greater attention. Overarchingly, findings suggest the need to develop stronger programming and more robust training supported by clear and actionable policies in homelessness services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".