Interdisciplinary and Transdisciplinary Research and Education in Canada: A Review and Suggested Framework
Bibliographic record
Abstract
Transcending disciplinary boundaries is becoming increasingly important for devising solutions to the world’s most pressing issues, such as climate change and food insecurity. Institutions of higher education often present challenges to teaching students how to work and innovate on transdisciplinary teams. We first define transdisciplinarity and like concepts, using these to review databases of three major funding agencies (CIHR, NSERC, and SSHRC) for awards given to inter- and transdisciplinary programs across ten fiscal years beginning 2005-2006 and ending 2014-2015 to identify trends in funding as an indicator of skill need. We then search for programs offering transdisciplinary learning opportunities at Canadian universities accounting for 71% of all students. Though the proportion of interdisciplinary and transdisciplinary funded research grants has risen considerably, we found only a few examples of interdisciplinary learning opportunities for students in post-secondary education programs. Generally, while students were able to take a range of courses, instruction remained discipline-specific. Specifically, Canadian undergraduates lack an in-program, experiential, transdisciplinary learning opportunity. We propose a framework (ICON) as a solution to fill this gap. Using senior independent study courses, which often have built-in curricular flexibility, students can participate with ICON while still obtaining credit towards their degrees. We conclude that transdisciplinary education opportunities are an essential part of the undergraduate experience and should be recognized across degree 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.063 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".