Racialized Risks, Queer Threats: Refugee Experiences of “Sanctuary” in the City of Toronto
Bibliographic record
Abstract
There appears to be a gap in the literature that examines the intersectionality of identities for the refugee subject, especially for queer refugees. As well, there is a prevalence of heteronormative discourses throughout the literature. In all cases, homophobic violence is named but I will argue this is not the problem; it is merely a symptom of a broken system rooted in discourses of securitization and heteronormativity. Currently, migration to Canada is overseen by an increasingly over-securitized state which treats refugee claimants as threats to the nation. Concomitantly, the cultural adherence to traditional, white, heteronormative identities adds another dimension of risk for racialized, queer refugee subjects. This research study examines the experiences of resettlement for racialized and queer refugees in Toronto – a city that claims to be a sanctuary for such refugee claimants. The findings show that although queer refugees are generally safe from blatant and overt forms of violence post-migration, they still feel the need to resort to strategic methods of discretion, as it takes time to unlearn the fear and insecurity that exists as a result of experiencing trauma in the previous country. The interviews demonstrate that although some queer refugees may have to overcome internal and external challenges in their resettled lives, ultimately the action of migrating to Canada has opened up a multitude of promising possibilities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".