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Record W2275672838

Reducing Incentives for Abuse: Canada's inland refugee system

2013· article· en· W2275672838 on OpenAlexaboutno aff
Navjit Kaur Khind

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeIncentiveCriminologyPolitical sciencePsychologyEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

The growing interdependence in the world and the increasing number of refugees has made public policy in this area one of the key challenges facing nations today. A number of countries have introduced policies aimed at deterring non-genuine refugee claims. Canada is lagging behind in developing solutions to its growing refugee problems, namely large backlogs and growing number of applications. This study identifies the challenges of Canada’s current inland refugee system to recommend policy options to reduce incentives for abuse by non-genuine claimants. Using a comparative case study analysis of Australia, the UK, and Sweden, it provides policy recommendations on how these can be addressed. The analysis shows that streamlining procedures, having one agency responsible for claim processing, and the provision of social benefits being tied to a claimant’s compliance with claim processing lead to an efficient refugee determination system. The policy options proposed focus on these policies as they have been successful in other countries but are missing in Canada. Designating all refugee claim-processing matters to the Immigration and Refugee Board of Canada is presented as the best of the three policy alternatives.

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 score0.511
Threshold uncertainty score0.431

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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