Learning for Transdisciplinary Leadership: Why Skilled Scholars Coming Together Is Not Enough
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it