Community-based research for food system policy development in the City of Guelph, Ontario
Bibliographic record
Abstract
Community-based research (CBR) has grown in popularity as a research approach, which aims to foster collaboration between academic researchers and community members or organisations. CBR is often initiated with the intention of creating constructive social change at the same time as generating knowledge or understanding of specific concerns raised by community members. The June 2011 Ontario Provincial Planners Institute Call to Action, entitled Planning for food systems in Ontario, identified the need for participatory planning for sustainable food systems in municipal policy planning. This article provides an example of one such planning process in Guelph, Ontario. Using principles of CBR, researchers from the University of Guelph partnered with a grassroots food security organisation in order to collaborate on food policy planning and make a contribution to the review process for the City's Official Plan. Bringing together best practices from literature, case study examples, and engagement with citizens through a focus group session, the process resulted in a submission of policy recommendations to City staff. This article aims to contribute to the practice of CBR by highlighting the benefits and barriers encountered in one CBR process.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".