Evaluating innovation in transdisciplinary sustainability education: TRANSECTS’international learning labs
Bibliographic record
Abstract
Evaluative research can advance sustainability education through the learning it can enable, at micro and systems levels. This proposition is explored by examining evaluation practice in a 6-year international programme entitled Transdisciplinary Education Collaboration for Transformations in Sustainability involving universities and biosphere reserves/regions in Germany, South Africa and Canada. A Transdisciplinary International Learning Lab (TILL) was evaluated using a theory-based evaluation approach and interviews, focus groups and questionnaires that yielded qualitative data. Through meta-reflection, we concluded that our TILL had elements of a Field School, rather than a Learning Lab, and that our curriculum required more explicit deliberation among programme developers and implementers towards a deeper and shared understanding of pedagogical assumptions and more congruent practice of transdisciplinary and transformative sustainability education. The reflective, theory-based approach enabled learning from evaluation and was captured in a shared refinement of the theory of change, which makes it explicit that learning from pedagogical innovations is not only for students but also for academics. The paper is an invitation to other innovators in sustainability science, education and evaluation in higher education, to share related findings.
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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 | 0.080 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".