The US military's malaria research in Kenya and the geopolitics of global health
Bibliographic record
Abstract
Abstract The US Army Medical Research Directorate – Africa, also known as the “Walter Reed Project,” is at the forefront of the US military's malaria research and surveillance practices. Since its establishment in Kenya in 1970, the Walter Reed Project's research capacity and infrastructure has significantly expanded, now placing it in a civilian‐led global health network. In this paper, I trace the development of the Walter Reed Project's malaria research and surveillance practices in Kenya from 1970 to today. I explore the geopolitical logics driving the Walter Reed Project's development, its growing infrastructure in Kenya, and its changing relationships with civilian institutes and global health networks. In doing so, I demonstrate the intersections between geopolitics and global health the Walter Reed Project reveals. The paper demonstrates how the Walter Reed Project brings geopolitical logics to bear on its collaborative global health research practices; how it reveals an often overlooked position of state power and geopolitics in mainstream global health networks; and how its health intervention sites in Kenya are points in a geopolitical topography of war‐making, connected to geographies of militarism, empire, and violence in disparate spaces. The paper reveals the attachments to war that underpin the military's global health engagement practices and that permeate mainstream global health networks and sites of health intervention.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".