Cultivating educational adaptability through collaborative transdisciplinary learning spaces
Bibliographic record
Abstract
Abstract Empowering students and scholars to effectively address complex societal challenges frequently entails embracing unconventional pathways to foster transdisciplinary (TD) education. This empowerment is further facilitated by collaborative efforts supported by the TD experience. This paper examines one such initiative: a student-centered, experimental design of a TD doctoral pilot program for environmental sustainability at the University of British Columbia, a large, research-intensive public university in Canada. In this study, we documented shifts in participants’ development and assessed the impact of TD collaboration conditions on the educational design process. The findings indicate that engaging in collaborative TD experiences yields substantial pedagogical benefits, introducing novel opportunities for design and experimentation. This TD space appears to offer conducive conditions for students and faculty to more effectively navigate adaptive and innovative contexts within higher education. Pedagogical experimentation of this nature provides insights that are challenging to derive from theoretical speculation alone, offering potential pathways for today’s learners and educators as they confront complex societal challenges.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".