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Record W2325664190 · doi:10.1093/jicj/mqu014

Joseph Rikhof, The Criminal Refugee: The Treatment of Asylum Seekers with a Criminal Background in International and Domestic Law

2014· article· en· W2325664190 on OpenAlexaboutno aff
Alan Grant

Bibliographic record

VenueJournal of International Criminal Justice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeePersecutionLawConventionPolitical scienceRefugee lawHuman rightsContext (archaeology)CriminologyCriminal lawInternally displaced personSociologyPoliticsGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.333
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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