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Record W4402548680 · doi:10.1093/jhmas/jrae026

Maladies of Empire: How Colonialism, Slavery, and War Transformed Medicine, Jim Downs

2024· article· en· W4402548680 on OpenAlexaff
Aparna Nair

Bibliographic record

VenueJournal of the History of Medicine and Allied Sciences · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsColonialismEmpireArtAncient historyHistoryClassicsArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.013
Scholarly communication0.0070.007
Open science0.0010.003
Research integrity0.0060.018
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.070
GPT teacher head0.266
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractno

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