Strengthening city–university partnerships to advance sustainability solutions: a study of research collaborations between the University of British Columbia and City of Vancouver
Bibliographic record
Abstract
Purpose This paper aims to investigate sustainability research collaborations between the City of Vancouver and the University of British Columbia (UBC), as a case study to better understand how to use city–university partnerships to advance effective urban sustainability policy and practices. The study compiles a basic inventory of partnerships since 2010, describes their benefits, areas for improvement, barriers to collaboration and proposes ways to increase and improve future collaborations. Design/methodology/approach The study draws on an electronic survey completed by 58 individuals and interviews with 13 such participants who were faculty members and staff at UBC and Vancouver. Findings Most collaborations responded to climate change in some way, were initiated through informal professional relationships and involved single departments in each organization. Projects ranged in size, duration and level of municipal funding. Although project participants were generally happy with past experiences, future collaborations could be improved by increasing leadership commitment and resources and producing more mutually beneficial outcomes. Barriers included lacking awareness of potential partners, difficulty aligning municipal needs with academic research interests and divergent expectations about project resources. The study recommends introducing formal processes to help identify overlapping interests and opportunities, enhance co-creation of projects and increase leadership and resources. Practical implications The findings may inform the development and implementation of future city–university partnerships to advance sustainable policies and practices in urban areas. Originality/value This paper contributes by reviewing experiences with city–university collaborations and offering evidence-based recommendations to improve them, thereby increasing opportunities for more effective urban sustainability solutions.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".