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Record W1824067442 · doi:10.22230/cjc.2015v40n3a2831

“Immigrants Can Be Deadly”: Critical Discourse Analysis of Racialization of Immigrant Health in the Canadian Press and Public Health Policies

2015· article· en· W1824067442 on OpenAlexaffvenueabout
Sylvia Reitmanova, Diana L. Gustafson, Rukhsana Ahmed

Bibliographic record

VenueCanadian Journal of Communication · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of OttawaMemorial University of NewfoundlandCarleton University
Fundersnot available
KeywordsRacializationImmigrationFraming (construction)Critical discourse analysisPublic healthSociologyInjusticePolitical sciencePower (physics)Social injusticeGender studiesCriminologyPoliticsLawMedicineRace (biology)History

Abstract

fetched live from OpenAlex

By examining the role of the Canadian press in framing health and social issues of immigrants, the authors highlight the issues of power and social injustice in which immigrant health is constructed and handled by Canada’s health policies. Critical discourse analysis of 273 articles from 10 major Canadian dailies over one decade showed that pre-existing racializing discourses, which treat the immigrant body both as a disease breeder and an irresponsible health fraudster, continue to materialize in contemporary Canadian press coverage. A more balanced and fair media coverage of immigrant health will require deracialization of immigrant health issues as well as the transformation of the Canadian press toward greater inclusivity.

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)0.014
metaresearch head score (Gemma)0.026
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 score0.147
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.010
Science and technology studies0.0540.049
Scholarly communication0.0230.007
Open science0.0030.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.113
GPT teacher head0.413
Teacher spread0.300 · 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

Citations36
Published2015
Admission routes3
Has abstractyes

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