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Record W2739891026 · doi:10.1111/hic3.12393

Western medicine in Africa, part II: Ethiopia, East and Southern Africa to 1900

2017· article· en· W2739891026 on OpenAlexaff
Jonathan Roberts

Bibliographic record

VenueHistory Compass · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsDisadvantageWestern medicineHegemonyMiddle EastFrench hornMedicineHistoryGeographyEthnologyAncient historyPolitical scienceAlternative medicinePsychologyArchaeologyLaw

Abstract

fetched live from OpenAlex

Abstract Before 1900, Western medicine struggled to attract patients in Africa. As part 2 of the history of European medical practice in Africa before 1900, this article describes how practitioners from Europe tried to win over patients in Ethiopia and East and Southern Africa, regions that already had pluralistic healing networks of herbal and spiritual therapies. Western medicine began at a disadvantage because it could offer little to combat the diseases of the tropics or fight epidemics sweeping the continent from Asia. However, over the course of several centuries, emissaries, missionaries, and explorers from Europe were able to bring a unique set of European healing ideas, practices, and medicines to the continent with them. African patients showed skepticism towards practices that they regarded as overly invasive, such as surgery at hospitals, but they were open to experimenting with pharmaceuticals as treatments for illnesses for which they had no cure. Western medicine did not “save” the people of Africa nor did it achieve hegemony, but by 1900, it was able to gain a footing on the Horn of Africa, on the coast of East Africa, and throughout Southern Africa.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.159
GPT teacher head0.301
Teacher spread0.141 · 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
GenreEmpirical

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

Citations2
Published2017
Admission routes1
Has abstractyes

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