Education for Sustainability, Transformational Learning Time and the Individual <–> Collective Dialectic
Bibliographic record
Abstract
In the interest of developing sustainability practitioners, this manuscript challenges the conceptualization of transformative learning for Education for Sustainability (EfS) in relation to single courses or programs. Conversely, I will argue that becoming a sustainability practitioner (i.e., someone who takes action in the interest of the sustainability movement) is life-long and life-wide commitment. Time and how and why it matters is addressed. To develop this point, this manuscript details a case study of an education for sustainability graduate program that I designed and currently lead. The purpose is to further theorize transformative learning as it links individual action(s) and collective change(s) in the border-like but permeable spaces that are in-between. It asks the practical question of the ways educators (and practitioners) might expansively and generatively work together in creating a lifetime of classrooms to continuously bridge individual action and collective change.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".