Overcoming problems with sexual minority refugee claims: is LGBT cultural competency training the solution?
Bibliographic record
Abstract
When problems related to sexual orientation and gender identity are raised in relation to refugee determination proceedings, many advocates, refugee lawyers and refugees consistently call on adjudication authorities to mandate continuing professional training for personnel involved in LGBT refugee cases. For instance, the 2011 report Fleeing Homophobia on LGBT asylum claims in Europe contains explicit references to training in its recommendations. The United Nations High Commissioner for Refugees has also raised the issue of training in their 2008 Guidance Note on Refugee Claims Relating to Sexual Orientation and Gender Identity. While calls for training have sometimes outlined specific competencies to be developed by adjudicators, for the most part proponents fail to be specific about what type of professional development will result in better decision-making in LGBT refugee cases. By first considering the objectives and goals of sexual orientation and gender identity training for refugee personnel, this paper will argue that ‘LGBT Cultural Competency Training,’ an approach developed in the health and social work fields, is an appropriate model for the refugee context. It would conceptualise more clearly the kinds of professional development interventions that are required to improve the refugee determination process, and it would also help in targeting training modules to be most effective. The author will also highlight the limitations of the tool and caution that LGBT Cultural Competency Training is not a cure-all for the full range of problems facing LGBT refugees.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".