Community-Campus Collaboration in the Canadian Food Movement
Bibliographic record
Abstract
This session explores lessons learned in collaborations between academic researchers, students, and community-based practitioners working for non-profit organizations active in Canada’s food movement. Collaboration on a joint project, even when there is a shared vision, is not always easy. We ultimately also have different goals to meet, needs, and access to resources. Such factors can complicate the collaborative project. At the same time, success produces results that no one individual or organization could have achieved on its own. It reports on results of the second year of projects supported by the Community Food Security hub of the Community First: Impacts of Community Engagement (CFICE) research project, grounded in a partnership between Carleton University and Food Secure Canada, and funded by the Social Sciences and Humanities Research Council of Canada. Presenters will speak to the lessons learned from evaluations of five community-campus partnerships from across Canada undertaken in 2013-14.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.059 | 0.013 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".