Shit Voyeurism, Anti-Blackness, and the Spherical: Rendering Antibiotic Use in Africa
Bibliographic record
Abstract
This piece interrogates anti-Black racism and coloniality in global health reporting on antibiotic use in Africa. I focus on New York Times discourse, imagery, and films, reading these intertextually with wider political and public health rhetoric. In critically attending to mediated imaginaries of Nairobi, Kenya as “unhygienic,” I demonstrate how this figuration comes to index local pharmaceutical practices that appear “non-Scientific.” The situated knowledge informing such practices is disregarded or, worse, presented as a threat to be targeted and eliminated. Such biomedical transgressions are indexed in the NYT by racialized references to waste, dirt, and excrement, turning the (structurally white) reader-viewer into what I call a shit voyeur. Building on Sylvia Wynter’s conceptualization of Man, I argue that Man-the-shit voyeur disregards his own culpability in rising antimicrobial resistance, locating health risks instead in the contaminating nature of Others. This racialized grammar is further subtended by the logic of the spherical—the illusion that the world-as-sphere is a totality that Man can perceive with a unidirectional gaze. Global health reporting that is racialized via the logics of the spherical and shit voyeurism not only fails to accurately represent medical concerns in Africa but also perpetuates biomedical hegemony and/as global white supremacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".