Recommendation: A reimagined One Health framework for wildlife conservation - R0/PR4
Bibliographic record
Abstract
The One Health discourse is dominated by the role of animal health as a determinant of human health. This discourse often disregards the intrinsic and ecological value of healthy animals and is thus an inadequate framing for wildlife conservation. Our paper reimagines One Health for conservation purposes based on five premises: (i) health is cumulative; (ii) there are multiple species with different health needs and goals in the same setting; (iii) One Health emphasizes “bundled” relationships unique to a setting, rather than independent and intersecting spheres of health; and One Health should be (iv) equity informed and (v) have a shared goal that can be achieved through intersectoral actions. The reimagined framework is centered on the guiding principle that all actions should ensure no species or generation is prevented from reaching good health by the actions to protect other species or generations. Grounded in the positive outcome of health equity, the framework uses three prompts to guide One Health planning – populations, places and goals. The paper discusses how the framework can be applied for research concerning wood bison herds under imminent threat in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.047 | 0.018 |
| Insufficient payload (model declined to judge) | 0.130 | 0.084 |
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".