“Fuck Latino Illegal Aliens”: The Settlement Experiences of LGBTQI+ Asylum Seekers in Canada
Bibliographic record
Abstract
Upon their first year of arrival, do LGBTQI+ asylum seekers feel properly supported within British Columbia? This paper reveals the stories of community members who hold intersecting identities, and have taken unique journeys to migrate to Canada. This study utilizes Critical Race Theory, Transnational Feminism, and Thobani’s concept of the exalted subject to show how fleeing from persecution results in new forms of systemic violence and discrimination not experienced by other migrants. I interviewed three LGBTQI+ refugees who arrived in British Columbia, Canada over the last 10 years who described the multiple sites settlement violence experienced by them in the health care, housing, legal services, dating apps, the labour market and social support agencies designed to assist in their very settlement. Using a narrative analysis, I argue that this community needs a specialized focus to support their unique needs while at the same time acknowledging and challenging how border imperialism and settler colonialism shapes their experience. This study suggests that LGBTQI+ asylum seekers are not properly supported when they first arrive in British Columbia, and must navigate issues around settlement needs, geographical locations, violence and discrimination, migration timelines and waiting periods, code-switching, gratitude, and COVID-19. This paper recommends future research to be conducted around the settlement needs for LGBTQI+ asylum seekers.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.041 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.010 |
| 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".