Sexual Orientation in Canada's Revised Refugee Determination System: An Empirical Snapshot
Bibliographic record
Abstract
This is one of several articles in a special issue to celebrate Nicole LaViolette's research contributions relating to intersections between gender and sexual orientation in Canadian and international law of forced migration. Inspired by three aspects of LaViolette's research, the article offers a snapshot of how Canada's recently revised refugee determination system addresses refugee claims involving allegations of persecution due to sexual orientation. Using data obtained through access to information requests about 18,221 principal applicant refugee determinations from 2013 to 2015, the article examines patterns in outcomes in cases categorized by the Immigration and Refugee Board as involving sexual orientation. The article also examines patterns in the reasoning offered in 247 published Refugee Appeal Division decisions involving sexual orientation. The author concludes that, despite clear progress, some sexual minority refugee claimants continue to struggle to have their refugee claims adjudicated fairly and that different sexual minority groups encounter unique challenges in this regard. The article ends with recommendations for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".