The Fecundity of the Race Discourse in Public Health and Epidemiology : Understanding the Limits of Explaining Health Disparities Using Race Categories in Brazil
Bibliographic record
Abstract
This paper problematizes the use of race categories in epidemiological and health surveys in Brazil.The (re)production of epidemiological data using racial categories is common practice in epidemiology and public health.Often presented as a means of explaining the persistence of gaps in health status across diverse groups, racial categories have enjoyed an uncritical advantage.The rationale of using racial categories in surveys by its proponents is that such categories facilitate the development of policies that address structural inequalities in health access, and foregrounding reasons why disparities in health outcomes for minoritized and racialized groups and how broader social determinants of health (SDOH) must be addressed.Justified by the persistence of poorer health outcomes for minoritized groups in pluralistic societies, health researchers often argue for the necessity to collect data using racial categories.Utilizing the Foucauldian inspired Critical Discourse Analytic (CDA) method, this paper comments on the data quality issues related to the use of racial categories and the methodological dilemma such a practice poses for public health researchers and epidemiologists.The paper further cautions that an uncritical use of racial categories in public health surveys, reinforce and perpetuate a sense that the measures are real, their meaning uncomplicated, and their properties substantial.
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".