Policy Perspectives on Education for Sustainable Development: Taking stock of the Field in the era of the UN Sustainable Development Goals
Bibliographic record
Abstract
Education for sustainable development (ESD) has become a popular avenue to advance progress toward global goals linked to climate change, social issues and economic growth. For more than three decades, ESD has risen to prominence in international policy fora as a fundamental step in the implementation of a wide range of global development objectives. Particularly, following the adoption of the UN Sustainable Development Goals (SDGs), enthusiasm for ESD has grown rapidly. However, the current moment represents a break in the wave of support for global projects like the SDGs. Catastrophes like the COVID-19 pandemic and violent regional conflicts have elicited scepticism for global cooperation and the efforts of agencies like the United Nations (UN). Consequently, in 2024, the UN Secretary-General convened the Summit of the Future (SOTF) to revitalize multilateralism and reconcile the grievances of nations. Among those grievances were demands for more critical assessments of the transformative potential and relevance of the SDGs. Provoked by the recent global momentum, this special issue highlights key perspectives regarding how the international education community has rolled out ESD initiatives during the SDG campaign, with special focus on SDG Target 4.7. The commentaries offer valuable insights into concrete actions to enrich ESD, the evolving contours of policy production, and, more deeply, critiques of the underlying forces shaping the field. Following the SOTF, the degree to which the UN was successful in assuaging scepticism and re-mobilizing support for its mission is still to be determined. Rebuilding the trust and faith of nations is a long-term effort, but certainly, the range of voices emerging offers enriching new pathways for the field of ESD and across the development spectrum.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.030 |
| Scholarly communication | 0.037 | 0.044 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.065 | 0.057 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".