Eco‐campus: applying the ecocity model to develop green university and college campuses
Bibliographic record
Abstract
Purpose The purpose of this paper is to argue that Richard Register's ecocity model offers a strategic framework to help guide sustainability initiatives in North American higher education (HE) institutions. Design/methodology/approach This conceptual paper examines the theory of the ecocity and investigates the implications for its proposed building strategies for university and colleges, as institutions seek to create more sustainable campuses. The paper examines previous efforts to achieve sustainability and how the concept of the eco‐campus can be practically and productively applied. Findings There is no single campus that has fully embraced every facet of sustainability, but numerous HE institutions are strong leaders in diverse areas. The eco‐campus model provides concrete principles that proactively address HE institutions' ecological footprints and develops sustainable community practices. Social implications Sustainability is a pressing social issue. As world leaders in research, innovation, and education, universities and colleges are key places to address this global issue and foster progressive action within current and future generations. The eco‐campus approach represents an opportunity to initiate a cultural paradigm shift, whereby university and colleges become global leaders in sustainability. Originality/value While sustainability is now a cornerstone of research and teaching, North American HE institutions are faced with the challenge of realigning institutional practices, processes and resources to fully institute sustainability on campus. The eco‐campus model provides an innovative guide around which to hinge the development of sustainable institutional practices, structure progressive action, and foster meaningful change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".