Brokering Community–campus Partnerships: An Analytical Framework
Bibliographic record
Abstract
Academic institutions and community-based organizations have increasingly recognized the value of working together to meet their different objectives and address common societal needs. In an effort to support the development and maintenance of these partnerships, a diversity of brokering initiatives has emerged. We describe these brokering initiatives broadly as coordinating mechanisms that act as an intermediary with an aim to develop collaborative and sustainable partnerships that provide mutual benefit. A broker can be an individual or an organization that helps connect and support relationships and share knowledge. To date, there has been little scholarly discussion or analysis of the various elements of these initiatives that contribute to successful community–campus partnerships. In an effort to better understand where these features may align and diverge, we reviewed a sample of community–campus brokering initiatives across North America and the United Kingdom to consider their different roles and activities. From this review, we developed a framework to delineate characteristics of different brokering initiatives to better understand their contributions to successful partnerships. The framework is divided into two parts. The first examines the different structural allegiances of the brokering initiatives by identifying their affiliation, principle purpose, and who received primary benefits. The second considers the dimensions of brokering activities in respect to their level of engagement, platforms used, scale of activity, and area of focus. The intention of the community campus engagement brokering framework is to provide an analytical tool for academics and community-based practitioners engaged in teaching and research partnerships. When developing a brokering initiative, these categories describing the different structures and dimensions encourage participants to think through the overall goals and objectives of the partnership and adapt the initiative accordingly.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".