Transdisciplinary training: what does it take to address today’s “wicked problems”?
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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it