Introduction: Disease Reservoirs: From Colonial Medicine to One Health
Bibliographic record
Abstract
The introduction of the special issue "Disease Reservoirs: Anthropological and Historical Approaches" sets out the origins and trajectories of disease reservoir frameworks. First, it charts the emergence and elaborations of the reservoirs concept within and across early 20th-century colonial contexts, emphasising its configuration within imperial projects that sought to identify, map and control spaces of contagion among humans, animals, and pathogens. Following this, it traces the position the reservoir framework assumed within post-colonial practices and imaginaries of global health, with particular reference to the emerging infectious disease paradigm. The introduction shows that, in contemporary usages, while the concept continues to frame animals, humans and their bodies as containers of previously identified pathogens, it also emphasises the imperative of anticipating as-of-yet unknown diseases, harboured in the bodies of certain animals, through networks and techniques of surveillance. Consequently, the introduction argues that the notion of disease reservoirs remains intimately intertwined with concerns over the classification, organization, and management of peoples, pathogens, animals, and space. Finally, the introduction outlines the seven papers that form this special issue, stressing how they dialogue, complement, and challenge previous historical and anthropological approaches to disease reservoirs, with an eye to opening up new avenues for cross-disciplinary exploration.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".