Drivers and Barriers of Implementing Sustainability Curricula in Higher Education - Assumptions and Evidence
Bibliographic record
Abstract
Progress on the Sustainable Development Goals (SDGs) depends, in part, on the sustainability competencies of professionals in various fields, and thus, on the implementation of sustainability curricula in higher education. While many universities now offer sustainability curricula, and many more aspire to, there is a lack of evidence on what supports or hinders such implementation. This article presents a meta-study on 133 case studies from universities around the world and synthesizes the main drivers and barriers, identifies information gaps, and tests prominent assumptions on implementing sustainability curricula in higher education. The findings confirm that such implementation is associated with strong leadership by the university; incentives and support through professional development; concurrent implementation of sustainability in research, campus operations, and outreach; formal involvement of internal and external stakeholders as well as sustainability champions, among others. Common research protocols for case studies are needed to yield comparable data on these influencing variables and to enhance reliability of cross-case comparisons. Most sustainability programs could utilize the findings for informing their implementation processes.
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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 | 0.112 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".