The Critique of Asylum Law: Gender-Based Persecution and the Problems of Defining Social Group Ground in US Asylum Law
Bibliographic record
Abstract
The prevailing difficulty in achieving protection for women asylum seekers lies mainly in the social group ground definition. It is by interpretation that standards are set, which in the case of US, seem to vary depending on the administrative body deciding the case. The current standards are not harmonized, rendering gender-based claims susceptible to a compounded interpretation. While the US identified this problem and pioneered the definition of social group ground subsequently adopted by other countries including the United Kingdom, Canada, and New Zealand, some legal decisions it has made concerning claims in which gender plays a greater role in determining refugee status leaves much to be desired. Moreover, the Immigration and Naturalization Service (INS) was forced to propose regulations in recognition of this problem. These regulations aim to address the prevailing concerns on the overall claims relating to gender-based persecution, especially “when” and “how” gender-based claims can be bases for granting asylum. But so far the proposed regulations have not been promulgated in their final form, and the US should do so with a view to harmonizing its asylum procedure in order to enhance protection, particularly for women asylum seekers in the US. This will go a long way in reinforcing Obama administration’s commitment to defending America’s ideals that “still light the world”.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".