(Book Review) Education and Learning for Sustainable Futures: 50 Years of Learning for Environment and Change
Bibliographic record
Abstract
Education is often talked about as being the opening salvo to combat Earth's triple planetary crisis -climate, nature, and pollution, and really any systemic instability of the 21st century.Yet what kind of education is actually required, and whether any of the previous efforts have meaningfully had an impact, remains somewhat unclear.As Miseliunaite et al. (2022) argue, "education does more than react to a changing world; education transforms the world," underscoring the need to critically examine not just educational intent, but educational design and impact.In Education and Learning for Sustainable Futures: 50 Years of Learning for Environment and Change, Macintyre, Tilbury, and Wals offer a concise historical timeline of the evolution of environmental and sustainability education from the 1972 Stockholm Conference to the present.At the Stockholm +50 conference, their argument was framed around a stark realization: "What has become clear is the importance of education and learning in addressing what we can term a crisis of culture" (Macintyre et al., 2025, p. 6).The book will be of particular interest to scholars, teacher educators, early-career educators, administrators, and policymakers seeking a framework grounded in fifty years of historical integration to support a shift in the current paradigm of sustainability education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.029 |
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".