“I want to feel safe”: Social service experiences of LGBTQ+ newcomers in Windsor-Essex, Ontario
Bibliographic record
Abstract
This paper is based on semi-structured interviews conducted between April and September of 2023 with 13 LGBTQ+ newcomers, immigrants, and/or refugees, and who accessed social services within Windsor Essex within the previous 5 years. The study was conducted in partnership with Trans Wellness Ontario and was designed to facilitate an understanding of the specific needs of this group and inform social services supporting LGBTQ people and migrants in Windsor-Essex. Participant responses demonstrated common experiences of isolation in adjusting to life in Canada. In the context of social services, these experiences were largely attributed to a lack of services targeting LGBTQ+ newcomers, as well as a lack of knowledge and resources in existing services. This gap in service provision results in incidents of cultural incompetency and discrimination or insensitivity toward LGBTQ+ identities. The lack of services specific to LGBTQ+ migrants’ needs contributes to a sense of isolation from both LGBTQ+ and immigrant communities in individuals who are members of both. Recommendations include the establishment of partnerships between organizations serving LGBTQ+ and immigrant communities, both in supporting one another’s existing services through competency training, and through the creation of new programming to foster connections between individuals and their communities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".