Universities as Change Makers
Bibliographic record
Abstract
Higher Education Institutions (HEI) play a strategically important role in the multidimensional transformations needed to achieve more sustainable ways of living in this world. By applying a holistic or “whole institution approach,” they can promote and implement sustainability in research, teaching, campus management, and carry out the “third mission” of universities to generate trans-disciplinary knowledge useful for society. This publication showcases various projects and initiatives developed by universities from Canada, Chile, China, Colombia, Germany, Israel, Mexico, Peru, and Russia to promote the topic of sustainability in governance, teaching, research, and campus management. By presenting the key issues, recommendations, and lessons learned from these initiatives, it outlines innovative and diverse approaches that contribute to fostering sustainability at HEIs. These cases highlight experiences from all over the world that can be adopted by interested HEIs anywhere, in particular those HEIs forced to operate under conditions of serious resource scarcity and in contexts where sustainability is not yet a major part of academic activities. We also hope that this publication will be the start for closer exchanges on sustainability initiatives among universities all over the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 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".