Advancing campus and community sustainability: strategic alliances in action
Bibliographic record
Abstract
Purpose – Universities can provide a leadership role to develop and mobilize knowledge to meet societal needs. In fulfilling this mission, universities can also serve as agents of sustainable development on campus and in communities they serve. The purpose of this article is to describe the drivers that have advanced the University of British Columbia Okanagan campus' operational and academic sustainability objectives; the initiatives and partnerships developed on campus and in the community in response to these drivers; and the outcomes and lessons learned. Design/methodology/approach – This article summarizes the experience of the University of British Columbia Okanagan campus in leveraging key drivers to develop sustainability initiatives and partnerships for greater operational efficiencies, cost savings, environmental stewardship and applied research. The university's leadership commitment to sustainability, economic opportunities and provincial legislative requirements are among the drivers discussed. This paper also provides an innovative partnership framework to support sustainable community development. Findings – Drivers of sustainability in higher education can contribute to the development of sustainability initiatives and partnerships that benefit institutions and communities and achieve operational and academic sustainability mandates. Practical implications – This article provides information that can be applied by institutions of higher education to advance sustainability within the context of current economic conditions and societal needs. Originality/value – The experience of the campus and the partnership framework presented in this paper is original. The framework provides a mechanism to engage students, faculty and the community in sustainable community development research. Key insights from multiple perspectives and lessons learned are shared.
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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.067 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 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".