One Health for All: Advancing Human and Ecosystem Health in Cities by Integrating an Environmental Justice Lens
Bibliographic record
Abstract
We are facing interwoven global threats to public health and ecosystem function that reveal the intrinsic connections between human and wildlife health. These challenges are especially pressing in cities, where social-ecological interactions are pronounced. The One Health concept provides an organizing framework that promotes the health and well-being of urban communities and ecosystems. However, for One Health to be successful, it must incorporate societal inequities in environmental disamenities, exposures, and policy. Such inequities affect all One Health interfaces, including the distribution of ecosystem services and disservices, the nature and frequency ofhuman–wildlife interactions, and legacies of land use. Here, we review the current literature on One Health perspectives, pinpoint areas in which to incorporate an environmental justice lens, and close with recommendations for future work. Intensifying social, political, and environmental unrest underscores a dire need for One Health solutions informed by environmental justice principles to help build healthier, more resilient cities.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".