Learning for Transdisciplinary Leadership: Why Skilled Scholars Coming Together Is Not Enough
Bibliographic record
Abstract
Abstract Transdisciplinary research is an emerging new normal for many scientists in applied research fields, including One Health, planetary health, and sustainability. However, simply bringing highly skilled students (and faculty members) together to generate real-world solutions and policy recommendations for complex problems often fails to consistently create the desired results in transdisciplinary settings. Our research goal was to improve understanding and applications of transdisciplinary learning processes within a One Health graduate education program. This qualitative study analyzes 5 years of action research data, identifying four transdisciplinary leadership skills and four conditions required for consistent skill development. Combining Vygotsky's theory of proximal development with identified transdisciplinary skills, we explain why educational scaffolding is needed to enable more successful design and delivery of transdisciplinary learning, particularly in One Health educational programs.
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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 | 0.043 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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".