Just treatment on a damaged planet: Can we crip one health? And should we?
Bibliographic record
Abstract
Disability justice (DJ) and One Health (OH) are two approaches to jointly addressing human, animal, and environmental health that emerged around the same time in overlapping geographies but twenty years later, remain largely separate. Here I first highlight how DJ movements have long operated across human, animal, and environmental health. I then introduce OH as a movement rooted in the organizing of scientists, healthcare practitioners, and policymakers seeking bridge health governance and care across human, animal, and environmental domains. Its tendency to reproduce colonial, anthropocentric, and ableist power structures in the present emerges from its origins in tropical medicine, disease ecology, and veterinary pathology practiced at sites like zoos and colonial/settler colonial research stations. Given this, I then ask: what would it mean to try to ‘crip’ science and health care currently operating within OH towards the political aims of DJ? In conversation with other critical OH interventions, I raise preliminary concerns emerging from the resonances and tensions between questions of what DJ and OH each want. I argue that considering OH and DJ together this way offers important insights for what constitutes just treatment on a damaged planet as well as the possibilities, dangers, and limitations of different approaches to treating injured more-than-human collectives. Throughout, I draw on digitally archived media and primary source materials, expert interviews with OH practitioners, and broader critical scholarship.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".