Bibliographic record
Abstract
In this paper I explore how adjudicators in the Canadian refugee determination system assess sexual orientation refugee claims. By focusing on discourse and terminology of questions utilized in the hearing (in which the refugee claimant answers questions posed by the Immigration and Refugee Board (IRB) Member), I will outline how these questions contain predetermined social knowledge and thus operate as a cultural formation through which particular arrangements of sexual and gendered practices and identities are privileged. However, documents and interviews with IRB staff reveal the presence of a ‘gut feeling’ or ‘sixth-sense’ in determining the credibility of a claimant’s sexual orientation. While some may argue that these feelings represent a level of sensitivity that humanizes the decision making process, I argue that they reveal adjudicators’ application of their own understandings and feelings about ‘authentic’ sexual identities and relationships derived from specific cultural, gendered, raced and classed experiences, which, in effect, re-inscribe a homonormative mode of gatekeeping that may have profound consequences for a claimant whose narrative and/or performance fails to stir the appropriate senses.
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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".