Achieving campus sustainability: top‐down, bottom‐up, or neither?
Bibliographic record
Abstract
Abstract Purpose – The dynamics of organizational change related to environmental sustainability on university campuses are examined in this article. Whereas case studies of campus sustainability efforts tend to classify leadership as either "top‐down" or "bottom‐up", this classification neglects consideration of the leadership roles of the institutional "middle" – namely the faculty and staff. Design/methodology/approach – The authors draw from research conducted on sustainability initiatives at the University of Guelph combined with a review of faculty and staff‐led initiatives at universities across Canada and the USA, as well as literature on best practices involving campus sustainability. Using concepts developed in business and leadership literature, faculty and staff are shown to be universities' equivalent to social "intrapreneurs", i.e. those who work for social and environmental good from within large organizations. Findings – Faculty and staff members are found to be critical leaders in efforts to achieve lasting progress towards campus sustainability, and conventional portrayals of campus sustainability initiatives often obscure this. Greater attention to the potential of faculty and staff leadership and how to effectively support their efforts is needed. Originality/value – In the paper, a case is made for emphasizing faculty and staff leadership in campus sustainability efforts and several successful strategies for overcoming barriers are presented.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".