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Record W7084575900 · doi:10.6069/338m-6g25

Experiences in Implementing City University Partnerships (CUPs): Case studies and insights from the Emerald Corridor Collaboratory

2025· article· en· W7084575900 on OpenAlexaboutno aff

Bibliographic record

VenueResearchWorks at the University of Washington (University of Washington) · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsCollaboratoryContext (archaeology)Equity (law)EmeraldGeneral partnershipNatural resourceExecutive summaryStakeholderDiscipline

Abstract

fetched live from OpenAlex

To address pressing urban social and environmental challenges such as homelessness, displacement, pollution, the impacts of climate change and natural disasters, and dealing with decades of inequity, cities and universities can harness their relative strengths and create partnerships that generate new knowledge, approaches, and practices. If these city-university partnerships (CUPs) are designed with intentionality and a deep understanding of the respective goals and assets of each partner, they can substantially contribute to improving sustainability, economic health, and equity in their cities and regions. CUPs are not a new practice. Most academic institutions have some relationship with their local municipalities, and there are examples of established and successful formal CUPS, that offer frameworks and guides for such partnerships. See resources and reference section for examples. Relatively little, however, has been written about the institutional challenges faced in implementing and sustaining CUPs. Likewise, few resources have explored the unique, place-based context (e.g. history, capacity or resources) of different CUPs. Nevertheless, experience suggests that these different contexts heavily influence the shape of the collaboration and selection of projects, and can lead to very different approaches to and outcomes for the partnership. Additionally, the potential value of bringing together CUPs in a region to address place-based shared challenges (as opposed to a topical theme) has not been addressed. With support from the Bullitt Foundation, the Emerald Corridor Collaboratory (ECC) was established to explore how such gaps might be addressed. The ECC is composed of four city-university partnerships: the University of British Columbia and the City of Vancouver, BC; Western Washington University and the City of Bellingham, WA; the University of Washington and the City of Seattle, WA; and Portland State University and the City of Portland, OR. The Emerald Corridor comprises a sociopolitical geography within what is known as the Pacific Northwest or Cascadia region, which includes the province of British Columbia, Canada and the states of Washington and Oregon, United States. The ECC project focused on four cities within the region that were selected as representative urban areas hosting significant institutions of higher education. These cities share a moderate rainforest-type climate and analogous threats of climate change, a progressive liberal political leaning, and accelerated growth in urban and suburban developments. Furthermore, in spite of the international border, these cities are facing similar challenges of social inequities, environmental injustices, and homelessness. The location of major universities in each of the cities offers opportunities for significant collaborations between civic leaders and the academic community. Each city had some version of an existing CUP, at very stages of development, which provided a foundation for the regional collaboration and made up the core team for the ECC project. The goal of the ECC project was to advance the strength and productivity of the individual CUPs, to elevate a conversation about the challenges and opportunities facing the region, and to explore the utility of a regional network of CUPs for addressing these challenges. Throughout the project, each respective CUP worked on a local individual pilot project that functioned as a case study while simultaneously participating in collective activities, events and discussion to share experiences, challenges and learning. We sought to assess and document our experiences and learning, to produce useful insights for further development or refinement of CUPs so they can serve as a force for positive change within their cities. These experiences and insights are contained in this report.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.257
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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