The Experiences of LGBTQ+ Migrant Youth with Discrimination in Canada: A Systematic Review
Bibliographic record
Abstract
Relative to the general population, members of LGBTQ+1 communities experience disproportionately higher rates of violence, stigma, and discrimination. When discrimination is considered in the context of racialization and migration, even higher rates of discrimination can be seen. Discrimination can take many forms, including gender- and/-or sexual harassment, in which derogatory language is used toward members of LGBTQ+ communities, for example. Discrimination may also manifest itself in restricting one’s access to housing or employment opportunities. Given the pervasive nature of discrimination, it is crucial to explore how various forms of discrimination can occur at the intersections between sexuality, gender identity, race, and ethnicity manifests among migrants2 who identify as LGBTQ+. However, there is a lack of knowledge about the experiences of discrimination among LGBTQ+ individuals with a migration background in Canada, particularly among youth. Gaining a deeper understanding of LGBTQ+ migrant youth experiences is particularly important given immigrants will represent up to 30% of Canada’s population by 2036 (Government of Canada, 2023). Moreover, 22% of the Canadian population currently self-identifies as a ‘visible minority’ and 4% identify as LGBTQ+ (Statistics Canada, 2022). Therefore, there is a significant proportion of LGBTQ+ migrants, including youth, in the Canadian population who are at risk of discrimination and violence based on the intersection between gender, gender identity, race and/or ethnicity. Further research is warranted regarding how LGBTQ+ migrants youth experience various forms of discrimination before, during and after migrating to Canada.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".