Community Food Systems Report Cards as Tools for Advancing Food Sovereignty in City-Regions
Bibliographic record
Abstract
Developing pragmatic possibilities for advancing food sovereignty to address challenges of justice and sustainability within food systems is an essential project for human survival. A practical starting point is to identify existing challenges along with comprehensive strategies that avoid isolated fixes. Community food systems report cards are a tool to inform and influence city-region food system governance by providing a connected and comprehensive snapshot of these systems, connecting people, places, and processes, and informing research, decision-making, and program planning. This article explores and reflects on the experiences of developing community food systems report cards in Thunder Bay and Durham Region in Ontario, Canada. Through sharing lessons learned, cautions, and limitations, we explore the report cards’ origins, development processes, findings, distribution, and impacts. We argue that community food systems report cards can be a valuable tool for understanding a city-region food system, monitoring progress, identifying gaps, and comparing and communicating experiences to communities, food system stakeholders, and decision-makers. However, community food systems report cards are only the starting point for advancing food sovereignty in city-region food systems.
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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 | 0.082 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".