Intercultural networks deepen learning for transformative sustainability education: lessons from co-designing transdisciplinary international learning labs
Bibliographic record
Abstract
In this paper, we emphasize the value of an intercultural network of researchers, students, and practitioners engaged in co-creating and delivering transdisciplinary sustainability learning opportunities. The network, the Trans disciplinary E ducation C ollaboration for T ransformations in S ustainability (TRANSECTS), is a north–south partnership with hub universities in Canada, Germany, and South Africa. Here, we introduce one pathway for learning — Transdisciplinary International Learning Labs (TILLs) — which are immersive learning experiences that take place in the United Nations Educational, Scientific and CulturalOrganization (UNESCO)-designated Biosphere Reserves/Regions. We describe preliminary lessons learned through collaborating across national and disciplinary boundaries to design, deliver, and evaluate this novel sustainability educational format. Drawing on a framework for transformative transdisciplinary learning, we explain how TILLs have contributed to single, double, and triple loop learning by students and the academics and practitioners who co-design and implement them. We share these lessons to inform other lab models that seek to provide transformative sustainability education.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".