Countering the logics of war in global health policy: fake drugs, antimicrobial resistance, and fugitive science
Bibliographic record
Abstract
Powerful pharmaceuticals are readily available for purchase throughout Tanzania and global health policy makers decry this situation as dangerous and disordered, as if no rules govern the use of drugs in Africa. In the prevailing global health understanding, ‘truth’ lies in the laboratory science that goes into the making and proper prescription of drugs, and such deviations as ‘overuse’ and ‘misuse’ result from the fact that locals supposedly misunderstand what these drugs are and how they should be used. However, my ethnographic research in Tanzania reveals that embodied epistemologies frequently enable medical practitioners and patients to evaluate the quality of various drugs and to identify chakachua (substandard or adulterated) pharmaceuticals through their material and sensory qualities—a practice I conceptualize as a form of ‘fugitive science’ (Rusert Citation2017). In light of this, I analyze the WHO’s National Action Plan for Antimicrobial Resistance in Tanzania, demonstrating how such global health policies disregard this knowledge, employing neocolonial rhetoric that presents ‘ignorance’ and ‘lack of hygiene’ as the sources of growing antimicrobial resistance while simultaneously obscuring structural inequalities. I argue that such forms of global health surveillance operate through the logics and epistemologies of war (Chow Citation2006; Terry Citation2017) in ways that render populations in the Global South into threats and targets. I conclude by suggesting that fugitive science can work as counter-evidence to health security frameworks and, as such, represents a furtive form of resistance to these militarized logics.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.149 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".