Community-based research for food system policy development in the City of Guelph, Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. Keywords: community-based researchurban agriculturemunicipal food policy Acknowledgements Our special thanks to the staff at the ICES University of Guelph, the City of Guelph and the GWFRT for their roles in this project. The authors also acknowledge partial funding from the Ontario Ministry of Food, Agriculture and Rural Affairs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it