Environmental learning and agency in diverse educational and cultural contexts
Bibliographic record
Abstract
[Extract] Environmental education is concerned with engaging learners in examining the relationship between humans and nature, or stated another way, between society and its social systems, on the one hand, and the biophysical or non-human natural environment and its ecological systems, on the other. And as Scott and Gough (2003) argue: "learning is central to the relationship between society and nature. People learn, organizations learn and, in a sense, the environment learns as nature responds to the results of human learning and activity." (p. 8) These authors further characterize environmental learning as "learning that accrues from an engagement with the environment or environmental ideas" (p. 14). Furthermore, with the emergence over the last 20 years of the language of sustainable development and sustainability in international policy, they argue that sustainable development itself is a learning process through which we need to learn to build our capacity to live more sustainably (Scott & Gough, 2003). Thus, learning is viewed as central to creating a more environmentally sustainable, and, I would add, more socially just, future. In other words, learning is involved in improving both the condition of the planet and the human condition. The previous chapters in this book examine environmental learning in a full range of educational settings in diverse international contexts, including Canada, Denmark, the Netherlands, South Africa, Sweden, the United Kingdom and the United States. The case studies from these different educational and cultural contexts illuminate the challenges of engaging children and adults in meaningful learning regarding the complexity of environmental issues, as well as document and offer insights into the promising possibilities of such engagement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".