Joseph Rikhof, The Criminal Refugee: The Treatment of Asylum Seekers with a Criminal Background in International and Domestic Law
Bibliographic record
Abstract
It is explicit in the 1951 United Nations Convention Relating to the Status of Refugees (Refugee Convention) that those who have committed serious crimes should not fall under its umbrella of protection. In the provocatively titled The Criminal Refugee: The Treatment of Asylum Seekers with a Criminal Background in International and Domestic Law, Canadian government lawyer Joseph Rikhof embarks on a detailed analysis of the law of refugee status in the context of its limitations and restrictions for those thought to have a criminal background. The work is a comprehensive (if somewhat spottily edited) volume, beginning with an exploration of the origins of asylum as a concept that is both rooted in antiquity and embedded in the development of international human rights law. From this vantage point, Rikhof proceeds to an analysis of two central concepts in refugee law: first, exclusion from refugee status (for those who are thought to have committed various proscribed acts); and secondly, refoulement (the removal of an individual to the country of origin, and the likelihood of persecution). The study is comparative in nature, focusing primarily on the views of the United Nations High Commissioner for Refugees and the approaches of those countries that have ‘contributed the most’ to the twin concepts of exclusion and refoulement.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".