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Record W4241301101 · doi:10.32920/ryerson.14665650.v1

The securitization of asylum seekers in Canadian political discourse

2021· preprint· en· W4241301101 on OpenAlexaffabout
Rajwant Deo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan University
Fundersnot available
KeywordsRefugeeSecuritizationCharterTamilPolitical scienceComprehensive Plan of ActionPoliticsAsylum seekerImmigration detentionRepresentation (politics)LawCriminologySociologyBusiness

Abstract

fetched live from OpenAlex

This study examines the representation of asylum seekers in Canadian political discourse published between 2009 and 2012. During this time period, Tamil asylum seekers arrived in Vancouver on the Ocean Lady and MV Sun Sea. Also in 2010 and in 2012 Bill C-11 and Bill C- 31 were introduced, which resulted in harsh changes to Canada’s asylum system. This study used securitization theory to understand how asylum seekers were presented as threats and the exceptional measures which were implemented to deal with them. It was found that asylum seekers were depicted in a very negative manner where they were accused of abusing the system, burdening the economy, and conspiring with migrant smugglers. This justified number of policies including the designated country of origin policy, mandatory detention for irregular arrivals, and cuts to refugee health care. These new policies were found to be inconsistent with the Canadian Charter of Rights and Freedoms.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.012
Science and technology studies0.0330.019
Scholarly communication0.0120.003
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.327
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2021
Admission routes2
Has abstractyes

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