Maladies of Empire: How Colonialism, Slavery, and War Transformed Medicine, Jim Downs
Bibliographic record
Abstract
Maladies of Empire revisits an argument that historians across the world have made repeatedly: that empire, war, and slavery have driven and shaped modern public health. Lucidly written, the book details how various European and American physicians (and one nurse in particular) produced and compiled epidemiological data, tested theories and techniques; it maps the field “not only at the familiar hubs of medical research but also at sites of imperialism, slavery, war and dispossession” (p. 5). In doing so, the book claims to decenter Eurocentric narratives of the origins of epidemiology. Maladies has a sweeping chronological and spatial framework, but Downs begins his book with an account of the “Black Hole of Calcutta” as an instance of scientific observation of the human body in the colonies. It is worth noting that the story of the Black Hole of Calcutta is a perfect example of the slipperiness, elisions, and deceptiveness of the colonial archive. When Siraj-ud-Daulah, the Nawab of Bengal, briefly captured the colonial city of Calcutta from the East India Company in 1756, he imprisoned several British captives in a jail cell. Several people died as a result, and the story itself would later become one of the founding myths of, and justifications for, the British empire in the region. Curiously, Maladies introduces us to John Zephaniah Holwell, the British surgeon who wrote the much-discussed account of this event, as a reliable narrator and “scientific” observer of crowds and air within the confines of the cell. But decades of scholarship has challenged much about Holwell’s account, from the number of people he claimed were in the cell (historians visiting the site noted that a cell this size could not conceivably have fit as many people as Holwell claimed), to the death toll.1 The choice of Holwell's account of the Black Hole of Calcutta to demonstrate “scientific observations” has the unfortunate effect of deracinating the chapter from its contexts of empire. From this beginning, the chapter then moves to describe how physicians working on slave ships, prisons, and colonies collected observations on scurvy, yellow fever, cholera, and the importance of “fresh air” and ventilation to people packed into tight quarters, and tracks the reforms and changes that resulted.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".