Perspectives and Practices of Education for Sustainable Development
Bibliographic record
Abstract
Informed by theory and full of practical advice, this key title offers a clear route to education for sustainable development (ESD) whilst questioning how we reconcile participatory, inclusive processes and the urgency of global crises. This handbook provides guidance for those with an interest in the purpose and direction of learning and the principles and practices of sustainability in universities in the UK and beyond. With critical analysis and useful case studies and recommendations, the book covers key topics such as: The need for sustainable development and the role that universities can play in this Showing how ESD should be part of a whole institution approach Development of relevant curricula with innovative and inspiring pedagogies to support sustainability competencies Transdisciplinary learning and pioneering forms of knowledge production Graduate futures and emerging priorities in the field of ESD Through detailed case studies from experts in the field, this book demonstrates how ESD enables a critical interrogation of our world and strengthens the capacities of our universities to nurture future thinking leaders. This is essential reading for all those interested in beginning or widening ESD in programmes, universities and the wider sector, including academic staff, university senior managers and support staff, students, policy makers, employers, and community leaders. This book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons (CC-BY-NC-ND) 4.0 license.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".