It’s the Basement Stories, not the Belt: Lessons from a community-university knowledge mobilisation collaboration
Bibliographic record
Abstract
Since 2006, United Way of York Region and York University have been collaborating to support community-university knowledge mobilisation and research collaborations that serve the human service needs of citizens in York Region. Ours is a sustained and sustainable community-university collaboration. What makes us sustainable? Certainly there is no single sustainability panacea (‘do this and you to will have a sustainable community-university collaboration’) but, in general, if you pay attention to the little details, the big things (like sustainability) will take care of themselves. Looking back we realise that the journey (our evolving collaboration) is more important than the destination (sustainability). We share our journey by interpreting a story about a family trip one of us (Daniele) made to see relatives. What happened when Daniele visited his relatives is an allegory for our community-university knowledge mobilisation story and is instructive for those forging community-university collaborations. We illustrate each of the lessons with examples from our experience. Keywords Community-university collaboration, knowledge mobilisation, shared history, evolution of partnership, sustainability
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.058 | 0.044 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".