Do Human Rights Laws Help Asylum-Seekers? An Empirical Study of Canadian Refugee Jurisprudence Since 1990
Bibliographic record
Abstract
This paper analyzes the circumstances under which international human rights treaties help or hurt asylum-seekers. Many scholars and lawyers assume that such treaties invariably assist those seeking refuge from persecution. Yet there have been no empirical studies to test this assumption. Until now. Through a mixed method empirical approach combining a database of over 4,000 asylum decisions over the past two decades and interviews with Canadian lawyers who specialize in representing asylum-seekers, this paper identifies several factors which help to determine the impact of human rights treaties in individual cases. It focuses on Canada because of that country’s reputation for openness toward refugees, as well as the receptivity its judiciary has traditionally shown toward international law. This paper advances three significant areas of socio-legal scholarship: the impact of international human rights law on state actors; the human rights approach to refugee law, and cause lawyering. The international human rights debate has reached a stalemate between those who believe that human rights laws have little or no impact on domestic processes and those who argue the opposite, citing advances in state compliance with human rights treaties. This paper proposes a more nuanced theory, positing that rather than an all or nothing issue, the impact of international human rights treaties in any given asylum case depends on a number of factors, including whether those treaties have been formally incorporated into domestic law, approved as precedent by the country’s highest court, and the gender of the applicant and judge. This paper also demonstrates that while such treaties help asylum-seekers in some cases, in others they may do more harm than good.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".