“How Can You Put This All Down in One Story?” Transgender Refugees’ Experiences of Forced Migration, Border Crossings, and the Asylum Process in Canada Through Oral History and Photovoice
Bibliographic record
Abstract
Research on asylum experiences for sexual and gender minority refugees has increased within the past decade. However, even within this growing body of research and critical commentary, the voices of gender minority or transgender (trans) refugees and their particular experiences navigating migration and asylum processes can sometimes be overlooked or lost within the larger subject of queer refugee experiences. Understanding the individual experiences of trans refugees can help scholars to further understand how gender identity and sexual orientation are regulated in migration and settlement. This article focuses on the narratives and photovoice of two trans refugees in Vancouver, British Columbia, Canada. Canada offered them relief from the state and social persecution they were experiencing in their countries of origin. Yet, they also experienced hyper-regulation by the Canadian state that caused them to be detained and interrogated by the Canada Border Services Agency (CBSA) as well as fear and silencing in their asylum hearings by the Immigration and Refugee Board (IRB) of Canada’s. Their experiences reveal how trans individuals are both hyper-regulated and marginalized by asylum and immigration processes in Canada. These government processes work to reinforce heteronormativity and the gender binary in which trans asylum seekers are marginalized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.026 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".