One Health for All: Advancing Human and Ecosystem Health in Cities by Integrating an Environmental Justice Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it