Community Food Systems Report Cards as Tools for Advancing Food Sovereignty in City-Regions
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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