A Seat at the Table: Implications of Structure and Diversity in Community Food Assessments
Bibliographic record
Abstract
Food insecurity associated with adverse physical and psychological health conditions is an issue faced by 12.5 percent of Canadian households. Current methods of food production and distribution serve to propagate rather than ameliorate these problems. A growing emphasis on the promotion of community food security aims to address not only the challenges of food security but also the underlying inequities and quality of life issues. Community food assessments are being employed in efforts to gain an understanding of the food system and its impacts. Conducted in conjunction with the Saskatoon Regional Food Assessment (SRFA), this study explores structures that contribute value and promote engagement among participants. While implementation is guided by best practices, currently the assessment process lacks theoretical grounding to allow a deeper understanding of the process. SRFA steering committee members were invited to participate in a two-stage interview examining their experience and perceptions of the process. Existing ideological perspectives of committee members played a significant role in their perceptions of the current food system and the effectiveness of implementing community food security approaches. Systemic change for enhanced community quality of life will require a highly structured collaboration and a strong central vision for participants to find common ground for mutual benefit.
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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 | 0.124 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".