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Record W2274392343 · doi:10.1177/1532708615611719

The Racialization of U.S. Public Health

2015· article· en· W2274392343 on OpenAlex
Patrick Cloos

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRacializationRacismSociologyMedicalizationBiopowerDisciplineGender studiesEthnic groupRace (biology)Power (physics)Representation (politics)Public healthPolitical sciencePoliticsSocial sciencePsychologyLawMedicineAnthropology

Abstract

fetched live from OpenAlex

The use of the category “race/ethnicity” intensified in the field of U.S. Public Health following the Clinton administration’s announcement of a budget increase for research focusing on ethnic and racial disparities in health. This article discusses the ways in which racialized difference is produced and represented as an object of knowledge and regulated by discursive practices in public health documents from U.S. federal government offices and a major public health journal published between 2001 and 2013. Races are approached, following Foucault’s proposition, as the product of racism, a technology of power of the modern State that consists of fragmenting humanity to permit colonizations. Thus, “race” has been established within the discourse to mark difference. Racism has developed concomitantly with the affirmation of power over life aimed at ruling out bodies and populations through public health practices, among others. The results suggest that the discourse on race varies throughout time. They indicate the relative permanence of a racialized regime of representation that consists of identifying, situating, and opposing subjects and groups based on standardized labels. This regime constitutes an ensemble of representational practices, which, together with disciplinary techniques and the use of culture as an idea, lead to the characterization and formation of racialized objects and stereotypes. These operations tend, together with medicalization and culturalization, to naturalize difference and constitute racial identities.

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.

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)0.017
metaresearch head score (Gemma)0.229
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.229
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.004
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 head0.617
GPT teacher head0.599
Teacher spread0.018 · 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