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Violence by any other name: constructing immigration crises, the threat of the sick refugee and rationalising immigration detention through moral panic

2021· article· en· W4205842678 on OpenAlexaffabout
Sarah Aberafi Adjekum, Ameil J. Joseph

Bibliographic record

VenueCritical and Radical Social Work · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRefugeeMainstreamImmigrationMental healthCriminologyMoral panicImmigration detentionIdeologyState (computer science)SociologyPoliticsGender studiesIdentity (music)Political scienceLawPsychologyPsychiatry

Abstract

fetched live from OpenAlex

This article is concerned with the employment of pathologising discourses of mental health and trauma by the mainstream media as they pertain to the treatment of migrants in detention in Canada. Using critical discourse analysis, this research contrasts mainstream media coverage of four major publications on immigration detention. It explores the media’s role in the (re)creation of refugee discourse, and as a purveyor of racial ideology, which problematises people of colour and demands state intervention in the form of mental health aid. The resulting discourse pathologises the refugee identity and simultaneously obscures the socio-political conditions and violence that necessitates their departure from their home countries. As refugee discourse is infused with biomedical understandings of mental health, it also legitimises the nation state’s practice of coercive social control for these populations through detention.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0190.082
Scholarly communication0.0160.008
Open science0.0010.009
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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