Transdisciplinary training: what does it take to address today’s “wicked problems”?
Bibliographic record
Abstract
There is a growing need to address today’s “wicked problems” seen in issues such as social justice, global climate crisis and endemic health concerns. Wicked problems are those for which there is no single, clear or optimal solution and thus are amenable to transdisciplinary solutions. Working in a transdisciplinary paradigm is thus seen as an increasingly necessary learned skill, and yet there is a dearth of knowledge on how curriculum centred around transdisciplinarity is perceived by those impacted by such curricula. This study examines the attitudes and responses of Aging Gracefully across Environments using Technology to Support Wellness, Engagement and Long Life NCE Inc.’s (AGE-WELL) stakeholders to the concept and role of transdisciplinarity in a training program intended to equip trainees and research staff from a variety of fields to address the “wicked problem” of aging well in Canada. We conducted 15 in-depth interviews with current AGE-WELL members, trainees as well as researchers and mentors, on the subject of designing the best possible training program. Our data illustrate the complexity of curriculum design and implementation to train for transdisciplinarity. We consider ways in which a shift in culture or ethos in academia may be required to pursue a thoroughly transdisciplinary approach to problem-solving. Short of instituting such a radical culture change as transdisciplinarity, however, strategic and conscientious efforts to integrate multiple and diverse perspectives, to attend carefully to communication and to foreground relationship building may well achieve some of the same goals.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".