Epidemiology and ‘developing countries’: Writing pesticides, poverty and political engagement in Latin America
Bibliographic record
Abstract
The growth of the field of global health has prompted renewed interest in discursive aspects of North-South biomedical encounters, but analysis of the role of disciplinary identities and writing conventions remains scarce. In this article, I examine ways of framing pesticide problems in 88 peer-reviewed epidemiology papers produced by Northerners and their collaborators studying pesticide-related health impacts in Latin America. I identify prominent geographic frames in which truncated and selective histories of Latin America are used to justify research projects in specific research sites, which nevertheless function rhetorically as generic 'developing country' settings. These frames legitimize health sector interventions as solutions to pesticide-related health problems, largely avoiding more politically charged possibilities. In contrast, some epidemiologists appear to be actively pushing the bounds of epidemiology's traditional journal article genre by engaging with considerations of political power, especially that of the international pesticide industry. I therefore employ a finer-grained analysis to a subsample of 20 papers to explore how the writing conventions of epidemiology interact with portrayals of poverty and pesticides in Latin America. Through analysis of a minor scientific controversy, authorial presence in epidemiology articles, and variance of framing strategies across genres, I show how the tension between 'objectivity' and 'advocacy' observed in Northern epidemiology and public health is expressed in North-South interaction. I end by discussing implications for postcolonial and socially engaged approaches to science and technology studies, as well as their relevance to the actual practice of global health research. In particular, the complicated interaction of the conflicted traditions of Northern epidemiology with Latin American settings on paper hints at a far more complex interaction in the form of public health programming involving researchers and research participants who differ by nationality, ethnicity, gender, profession, and class.
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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 | 0.041 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".