Learning for Sustainability: Considering Pathways to Transformation
Bibliographic record
Abstract
Social-ecological systems face increasing disruptions and challenges, many deriving from human actions, and learning is frequently touted as “the way out” for addressing them. Using a systematic review of 26 studies that span about 20 years and cover four continents, this article interrogates the link between learning, action, and societal transformation toward sustainability. Transformative learning theory provides the analytical framework. Studies indicated abundant instrumental learning outcomes, and substantial communicative learning, while personal transformation was less common. Individual, interpersonal, and collective sustainability action resulted from various kinds of learning, underscoring the important role that learning can play in shaping individual sustainability behavior. Instrumental learning, in particular, provided the skills and knowledge necessary for action. While study findings confirm the fundamental importance of learning, actions were largely individual and had lesser impact at the societal level.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.016 | 0.041 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".