Bibliographic record
Abstract
Medicine has been an integral part of the imperial project throughout the colonial history of Africa. From the very beginning of colonial expansion, the success of virtually all European endeavors was determined by the capabilities of medicine: disease became the most serious threat to white settlers. Even today, Africa, particularly its tropical regions, is considered one of the most problematic regions on the planet in terms of health threats and the organization of healthcare. In the first quarter of the 20th century, the situation was simply catastrophic. Leading colonial powers were forced to establish medical services in their overseas territories. This was primarily necessary to ensure the combat readiness of colonial troops and the health of the colonial administration and other Europeans, and secondly, to prevent widespread illness and excessive mortality among the local population, the main source of income from the exploitation of their overseas possessions. Both the British and French authorities achieved significant success in this area, although limited resources meant prioritizing care for European settlers. Due to a severe shortage of medical personnel, the colonial administration began recruiting Russian émigré doctors. This article examines the gender aspects of the émigré medical corps, as well as the professional activities and social adaptation of several Russian female doctors, paramedics, and nurses who worked in various regions of colonial Africa in the first half of the 20th century.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".