Context matters: Leveraging anthropology within one health
Bibliographic record
Abstract
Anthropologists develop long-term engagements with communities, animals, and the ecosystems they all share. This approach can provide important context that is necessary for One Health research, which may otherwise overlook the perspectives and lived experiences of community members. This paper presents two case studies that illustrate the importance of leveraging long-term, holistic, engagements with communities in moving the One Health concept forward. The first illustrates the complexity of understanding the health of people and animals within the context of environmental change in South India. The second provides insights into how the conservation of endangered species requires considering the entanglements of people, domestic animals, and the landscapes they share with wildlife in Madagascar. We demonstrate the value of integrating anthropological perspectives within interdisciplinary One Health research and interventions to better understand the complexity of systems.
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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 | 0.042 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.093 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.005 | 0.008 |
| 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".