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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".