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Record W1995531446 · doi:10.7202/1029211ar

Designated Inhospitality: The Treatment of Asylum Seekers Who Arrive by Boat in Canada and Australia

2015· article· en· W1995531446 on OpenAlexaffvenueabout
Luke Taylor

Bibliographic record

VenueMcGill Law Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsRefugeeCharterPoliticsImmigration detentionImmigrationAsylum seekerLawPolitical scienceParallelsHospitalityImmigration policySociologyCriminologyTourismEconomics

Abstract

fetched live from OpenAlex

This paper argues that there are distinct parallels between changes to the Immigration and Refugee Protection Act enacted by Bill C-31 (2012), in particular the Designated Foreign National regime (DFN), and Australia’s treatment of asylum seekers who arrive by boat. It is contended that recent Australian history and policy demonstrate the perils of adopting an ideology of control and exclusion toward asylum seekers instead of a politics of hospitality, and that Australia’s present political climate provides a stark and salutary warning to Canada, as it follows a similar path of securitization. The paper first explains what is meant by a politics of hospitality. In Part I, it analyzes Australia’s attitude toward, and its treatment of, asylum seekers, focusing in particular on the period since 1989. It is argued that Australia’s inhospitable stance toward asylum seekers has had discernible negative outcomes that provide important lessons for Canada. Part II provides a brief historical overview of Canadian policy toward asylum seekers, followed by an analysis of the DFN regime with reference to international law. It then argues that the DFN provisions contravene the Canadian Charter of Rights and Freedoms . The paper concludes by suggesting that Canada is at risk of following Australia’s security-oriented, inhospitable stance toward asylum seekers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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 designNot applicable
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

Citations7
Published2015
Admission routes3
Has abstractyes

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