Refugee Law and State Accountability for Violence Against Women: A Comparative Analysis of Legal Approaches to Recognizing Asylum Claims Based on Gender Persecution
Bibliographic record
Abstract
This paper addresses the inter-relationship between gender persecution, refugee law and state responsibility for domestic and sexual violence in women’s lives. The focus is largely on the Canadian context with some comparative attention paid to U.S. and British legal approaches to fitting women’s asylum claims - particularly those claims based on gendered violence - into existing legal categories, the most important of which is the category of “particular social group.” An analysis of the existing statutory framework governing the admission of refugees into Canada, including the Guidelines issued on Women Refugee Claimants Fearing Gender-Related Persecution, reveals the complex set of social and political issues surrounding Canada’s attempt to create legal spaces in which these claims can be accommodated. I examine two of these socio-legal issues. The first is the theoretically restrictive ways in which legal definitions of membership in a particular social group have created problems for women seeking asylum on the basis of gender persecution. Recent developments in U.K. jurisprudence on defining membership in a particular social group could productively inform Canadian law dealing with asylum claims based on gender persecution. I further argue that in order for the state to fulfill its commitment to a fair refugee determination system and to upholding gender equality, gender should be made an explicitly enumerated ground in the statutory definition of reasons for fearing persecution. Second, I analyse the Canadian state’s relationship to and accountability for the existence of gender persecution, particularly in the form of domestic and sexual violence perpetrated against its female citizenry. In particular, I examine the Canadian state’s own record on providing protection to its women citizens whose lives have been harmed by gendered violence, in order to throw into stark relief the paradoxical nature of the implicit assumption operating in many Western states that this problem has somehow been remedied at home. On this issue I expose the difficulties facing all states with regard to the pervasive problem of sexual violence in women’s lives, contradictions which complicate and challenge the “us/them” dichotomy implicitly underpinning the distinction between refugee-receiving and refugee-producing states.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".