Decoding Sexual Orientation in Refugee Status Determination: The Influence of Accounts of Emotions on Decision‐Making in Sweden
Bibliographic record
Abstract
ABSTRACT The understanding of sexual orientation as the basis for a Particular Social Group under the Refugee Convention is a contentious issue. Conventional thought suggests that sexual orientation (SO) claimants often face unfavourable treatment by asylum decision makers. Although much extant knowledge derives from in‐depth qualitative and doctrinal studies, only a few jurisdictions (e.g., Canada, the UK) have been analysed using large‐scale data or experimental methods. To complement this work, this article analyses a representative sample of SO asylum decisions from Sweden, aiming to elucidate the factors influencing refugee status determination outcomes. The findings reveal that emotional responses narrated in relation to the discovery of SO play a significant role in shaping decision makers' understanding of sexuality. This emphasis on internal accounts over observable practices poses challenges in verifying the credibility of SO claimants, raising questions about the reliability of credibility assessments. These insights underscore the need for nuanced approaches to assessing the credibility of SO claimants and ensuring fair treatment in asylum processes.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".