Creating Sustainable Universities: Organizational Pathways of Transformation
Bibliographic record
Abstract
2030 Agenda for Sustainable Development including the SDGs, is being integrated into sustainability strategies, research, teaching, pedagogy, and campus practices, and to position higher education institutions as key drivers for achieving the SDGs.Without universities as the demonstration of sustainable development, individuals and social changes needed for the creation of a sustainable future for mankind will be difficult.Key aspects of conceptualization of a sustainable university and pathways of organizational transformation are identified in this paper based on a comprehensive literature review and cross case analysis.17 world leading sustainable universities are selected from Australia, China, Canada, United Kingdom, United States and German.Data collection included in-depth interviews, reviews of documentary sources and analysis of routine data and information from offices of sustainability and websites of case universities.The cross case analysis of this paper depicts an effective, responsible and robust governance structure of world leading sustainable universities.The organizational pathways of transformation of sustainable universities have four key management elements: value, strategy, partnership, transparency.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".