Bibliographic record
Abstract
This commentary illustrates some of my journey as an epidemiology student to supplement my training with teachings about the sociopolitical histories, embedded racism, and colonial entanglements of the discipline. Drawing on critical scholarship on race and racism, I posit that epidemiology is a political arena that is inextricable from racist logics and (neo)colonial legacies and underscore the contemporary urgency of teaching it as such. I first provide a brief reflection on the ways in which white supremacist logics continue to resonate in contemporary epidemiological practice. I then trouble how race is constructed and taught in dominant epidemiological paradigms, particularly with respect to its operationalization as a risk factor. My aim is to foster a dialogue about the responsible use of race and race-based data in epidemiological practice, and call attention to the question: how can critical scholarship on race be integrated within entry-level epidemiological training?
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 | 0.120 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.063 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.015 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".