Academic Institutions and One Health
Bibliographic record
Abstract
To improve health at the human, animal, and ecosystem interface, defined as One Health, training of researchers must transcend individual disciplines to develop a new process of collaboration. The transdisciplinary research approach integrates frameworks and methodologies beyond academic disciplines and includes involvement of and input from policy makers and members of the community. The authors argue that there should be a significant shift in academic institutions' research capacity to achieve the added value of a transdisciplinary approach for addressing One Health problems. This Perspective is a call to action for academic institutions to provide the foundations for this salient shift. The authors begin by describing the transdisciplinary approach, propose methods for building transdisciplinary research capacity, and highlight three value propositions that support the case. Examples are provided to illustrate how the transdisciplinary approach to research adds value through improved sustainability of impact, increased cost-effectiveness, and enhanced abilities to mitigate potentially harmful unintended consequences. The authors conclude with three key recommendations for academic institutions: (1) a focus on creating enabling environments for One Health and transdisciplinary research, (2) the development of novel funding structures for transdisciplinary research, and (3) training of "transmitters" using real-world-oriented educational programs that break down research silos through collaboration across disciplines.
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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".