Identity, Refugeeness, Belonging: Experiences of Sexual Minority Refugees in Canada
Bibliographic record
Abstract
Cet article explore les résultats d'un projet de recherche communautaire qualitatif sur les expériences intersectionnelles des réfugiés minorités sexuelles vivant à Montréal et Toronto. Menée entre 2007 à 2010, cette étude a examiné les expériences des réfugiés minorités sexuelles, incluent leur immigration au Canada ainsi que leur processus de détermination du statut de réfugié. Nous étudions la façon dont les mesures politiques sur les réfugiés, les institutions sociales et les discours dominants contribuent à la construction sociopolitique des réfugiés minorités sexuelles. Nous concluons par une réflexion critique à propos des stratégies pour accroître la protection des réfugiés minorités sexuelles. This article explores the results of a qualitative community‐based research project on the intersectional experiences of sexual minority refugees living in Canada. Undertaken between 2008 and 2010, this study examines sexual minority refugees' multifaceted experiences of migration, the refugee determination process, and settlement. Through an analysis of the inter‐related themes of identity, refugeeness, and belonging, we hope to further investigate the ways in which Canadian refugee policies, social institutions, and dominant discourses contribute to the sociopolitical construction of sexual minority refugees. We conclude with an exploration of strategies for increasing protection of sexual minority refugees in 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| 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".