Becoming queer in Canada: Sexual orientation/gender identity (SOGI) refugee identities and the Canadian immigration apparatus
Bibliographic record
Abstract
The ongoing global refugee crisis has been, and continues to be, one of the most pressing humanitarian issues facing the world today. The global trends of destabilization, conflict, and persecution that have been fueling this crisis show no signs of stopping. Clearly, the issues that must be considered are too numerous to cover exhaustively in a single paper. Refugee and migration studies is a vast and complex topic with many specializations and subfields. Thus, for the sake of feasibility and actually generating meaningful information, it seems necessary to focus specifically on both a category of asylum seekers and a country to which they are applying. By doing so, the goal of this paper will be to participate in a more nuanced, and therefore more personal, exploration of a specific set of issues within a specific refugee intake apparatus that may then potentially be used to explore how current immigration systems may be improved. Therefore, this paper shall focus specifically on the experience of SOGI (Sexual Orientation and Gender Identity) asylum seekers, perhaps more commonly known outside of legal documents as LGBTQ+, as they navigate the Canadian refugee intake apparatus.,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".