A genealogy of epidemiological reason: Saving lives, social surveys and global population
Bibliographic record
Abstract
Metrics have become all pervasive in global health today. Instead of highlighting their advantages or shortcomings, this article builds on Hacking's notion of historical ontology and explores their political, conceptual and material conditions of possibility. Drawing on research on the Bloomberg Initiative to Reduce Tobacco Use in Developing Countries, one of the largest international efforts to address the non-communicable disease epidemic in the global South, the article starts by introducing the notion of epidemiological reason - a thought style associated with modern epidemiology that undergirds the metrics permeating the global health field and which is made of a multiplicity of elements, from the ethical imperative to save lives to the social-scientific technique of the survey and the concept of global population. The article then goes on to explore the genealogy of this thought style, arguing that three epistemological ruptures have been critical to its development: the reconfiguration of power articulated around a biopolitics of population in the eighteenth and nineteenth centuries; the twentieth-century shift in medical thought marked by the emergence of surveillance medicine and the idea of lifestyle; and the re-organisation of world health informed by globalisation theories at the start of the twenty-first century.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.112 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".