Relational One Health: A more-than-biomedical framework for more-than-human health, and lessons learned from Brazil, Ethiopia, and Israel
Bibliographic record
Abstract
The One Health conceptual framework envisions human, animal, and environmental health as interconnected. This framework has achieved remarkable progress in the control of zoonotic diseases, but it commonly neglects the environmental domain, implicitly prioritizes human life over the life of other beings, and fails to consider the political, cultural, social, historical, and economic contexts that shape the health of multispecies collectives. We have developed a novel theoretical framework, Relational One Health, which expands the boundaries of One Health, clearly defines the environmental domain, and provides an avenue for engagement with critical theory. We present a systematic literature review of One Health frameworks to demonstrate the novelty of Relational One Health, and to orient it with respect to other critically-engaged frameworks for One Health. Our results indicate that while Relational One Health complements several earlier frameworks, these other frameworks are either not intended for research, or for narrow sets of research questions. We then demonstrate the utility of Relational One Health for One Health research through case studies in Brazil, Israel, and Ethiopia. Empirical research which is grounded in theory can speak collectively, increasing the impact of individual studies and the field as a whole. One Health is uniquely poised to address several wicked challenges facing the 21st century-climate change, pandemics, neglected zoonoses, and biodiversity collapse-and a unifying theoretical tradition is key to generating the evidence needed to meet these challenges.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".