Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".