An Institutional Process for Brokering Community-Campus Research Collaborations
Bibliographic record
Abstract
Knowledge mobilization seeks to identify and support authentic research collaborations between community and university so that benefits of the research accrue to both partners. Knowledge brokering is a key knowledge mobilization mechanism that helps community and university partners connect and build relationships in order to share expertise for mutual opportunity. There remains a need to describe in detail the typical knowledge brokering devices and methodologies. This paper presents a detailed description of York University’s knowledge brokering service which is based on eight years of knowledge mobilization practice. The process is broken into 5 broad stages: 1) in progress; 2) no match; 3) match and no activity; 4) match and activity; 5) match and project. Stage 5 includes a step to identify the non-academic impacts of the collaborative research project. This process is illustrated using examples from York University’s practice in which a match was brokered for 82% of the 342 knowledge mobilization opportunities received between 2006-2014. York University partners with United Way York Region (UWYR) to create a regional approach to knowledge mobilization supports. This paper illustrates the impacts on community and university knowledge mobilization partners following the introduction of a community-based knowledge broker at UWYR.
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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 | 0.052 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".