Building Effective Relationships for Community-Engaged Scholarship in Canadian Food Studies
Bibliographic record
Abstract
How can community-engaged scholars best undertake grounded, policy-relevant, food systems research and teaching in ways that support the capacity of—and meaningfully build on—the experiences of civil society organizations working on these issues in Canada? This paper analyzes four case studies in the context of a research project that brings together members of the Canadian Association for Food Studies and Food Secure Canada. One case was led by Region of Waterloo Public Health and faculty from the University of Waterloo; a second by the Food Security Research Network at Lakehead University in Thunder Bay and the North Superior Workforce Planning Board; a third by the national student organization Meal Exchange and Ryerson University in Toronto; and a fourth by the BC Food Systems Network. We argue that the answer to the question above lies in establishing respectful relationships and recognizing the different cultures involved, and we offer five methodological insights for building effective relationships in practice. The first is the need to disaggregate the concept of ‘community’ in order to acknowledge the distinct needs and assets of the diverse organizations and populations involved. Our second and third insights are linked: Establish the relationship around a shared vision, and then negotiate mutually-beneficial teaching or research projects. Fourth, practitioners should approach community-campus engagement through the framework of contextual fluidity, which includes seeing the relationships and the vision at the heart of the work, while remaining open to shifts and new opportunities. Finally, adopting community capacity building practices helps practitioners realize their shared vision.
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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 | 0.045 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.079 | 0.071 |
| Scholarly communication | 0.038 | 0.012 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".