Does One Health need an ontological turn?
Bibliographic record
Abstract
One Health has gained global prominence in recent years. Alongside its emergence, there have been extensive social science critiques. In this contribution, we make the case for the value of recent theoretical discussions in the field of anthropology - sometimes referred to as an 'ontological turn'. We argue that taking theory seriously benefits One Health as an integrated approach that has interdisciplinary collaborations at its heart, but which encounters challenges when conversations based on different epistemological and ontological positions result in voices talking past each other. In this contribution, we offer two examples of what One Health specialists can gain from anthropologically-informed ontological thinking. Both require questioning ontological premises. Firstly, questioning assumptions about distinctions between animals and humans. Secondly, questioning the universality of biomedical knowledge. In the conclusion, we underline the importance of an ontological openness when it comes to the constitution and position of the actors as well as different bodies of knowledge that are involved in One Health and we show that talking to each other with awareness of different ontological positions is not impossible.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.134 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".