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
One Health is recognized as an increasingly important approach to global health. It has the potential to inform interventions and governance approaches to prevent future pandemics. Successfully implementing the One Health approach in policy will require active engagement from the public, which begs the question: how aware is the public of One Health? In this study, we examine the level and distribution of One Health awareness among the general public in China using a survey conducted in Beijing (n = 1820). We distinguish between awareness of the term of “One Health” versus awareness of the core set of ideas – the interconnection between the health of people, animals, and the environment. Our analysis shows that 40% of respondents reported that they have heard of the term, but more than double the number indicated that they recognize the core idea of interconnection between people, animals, and the environment. Specifically, about 83% of the respondents said that they believe people's health is closely connected to animal health and 86% believe people's health is closely connected to plant and environmental health. Multiple regression analysis indicates that women, younger people, and individuals with a higher level of education show higher levels of One Health awareness than their counterparts. Being aware of the term is associated with higher recognition of the core ideas. Policymakers and health practitioners should consider these findings when designing public awareness campaigns and educational initiatives to promote One Health principles.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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