‘The gloomy forebodings of this dread disease’, climate, famine and sleeping sickness in East Africa
Bibliographic record
Abstract
Identifying the nature of the association between climate, environmental, socio‐economic and political context and disease remains a major challenge, yet a better comprehension of the linkages is imperative if predictive models to guide public health responses are to be devised. Our understanding of the relationships could be improved through investigations of historical epidemics. In this paper we draw on a range of published and unpublished documents to explore the complex relationship between climate, environmental change and epidemic disease (re)emergence in East Africa, and Uganda in particular. This is a region which has experienced climate variability at a range of temporal and spatial scales, but which also has a long history of episodic epidemic disease. We focus on the late nineteenth and early twentieth centuries – a time of social, economic and political reordering in East Africa associated with European colonial intervention, but also a period which witnessed a variety of climatic, ecological and disease events. It will be argued that these developments coalesced, creating a set of spatially distinctive social and environmental conditions which fostered the emergence and prolongation of one of the most deadly episodes of disease in East African history, the sleeping sickness epidemic of c.1900–20.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".