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Record W2165081010 · doi:10.1177/1049732312441087

Exploring the Mutual Constitution of Racializing and Medicalizing Discourses of Immigrant Tuberculosis in the Canadian Press

2012· article· en· W2165081010 on OpenAlexaffabout
Sylvia Reitmanova, Diana L. Gustafson

Bibliographic record

VenueQualitative Health Research · 2012
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
Fundersnot available
KeywordsImmigrationTuberculosisGender studiesSocioeconomic statusPoliticsSociologyConstitutionPolitical scienceCriminologyMedicineLawPopulationDemography

Abstract

fetched live from OpenAlex

Drawing on critical discourse analysis of Canadian press coverage of the immigrant tuberculosis problem, we expose the complex relationship between press-constructed discourses of immigrant health and current tuberculosis control policies in Canada. The focus of these policies is on screening and surveillance of immigrants rather than addressing social inequalities underlying the problem of immigrant tuberculosis. The biomedical focus and racializing character of current policies were reinforced in the Canadian press by depicting tuberculosis as a biomedical (rather than a social) disease imported to Canada by immigrants. The status of the immigrant body as health threat was produced by and through preexisting and mutually constitutive racializing and medicalizing discourses materialized in press coverage and tuberculosis control policies. Deracialization and demedicalization of health information disseminated in the press are potentially important factors to be considered when revising health policies that would address the socioeconomic and political factors that determine the health status of Canadian immigrants.

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)0.023
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.279
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.628
GPT teacher head0.586
Teacher spread0.042 · 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 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

Citations18
Published2012
Admission routes2
Has abstractyes

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