Analysis of convergence between a unified One Health policy framework and imbalanced research portfolio
Bibliographic record
Abstract
Abstract The One Health (OH) approach is collaborative, multisectoral, and transdisciplinary, acknowledging the interdependence among animal, human and environmental health. It has garnered attention within the scientific community, particularly in response to the rising prevalence and global spread of emerging and re-emerging infectious diseases. Common OH issues include zoonotic diseases, antimicrobial resistance (AMR), food and water safety, and the human-animal bond. Among various OH topics, AMR represents a well-described, long-term, complex issue, with a substantial global death toll and large economic costs. Whereas interdisciplinary and transdisciplinary teamwork seems appropriate to address such complex challenges, effects on knowledge production are poorly known. In this study, we investigate how the scientific community mobilizes “One Health.” A comparative bibliometric analysis of OH and AMR research enabled us to assess the level of transdisciplinary research, identify emerging themes, through a co-occurrence network analysis of keywords, and disciplines mobilized, through a co-citation network analysis of scientific journals, in research, as well as level of international collaboration through analysis of co-authorship among countries. We detected a lack of consideration for non-communicable diseases (e.g., obesity, diabetes, cardiovascular diseases) and the well-being of human and animal populations in analysis of themes. Furthermore, although many disciplines are involved in OH and AMR research, little attention was given to social sciences, environmental health, economics, and politics. There was a strong influence of major global economic powers, including the United States and China, in scientific research on OH and AMR, as well as substantial collaboration among European countries. The present results indicated that guidelines are needed to address the mentioned concerns, and specific funds are required for underrepresented countries.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".