Mapping Food Policy Groups
Bibliographic record
Abstract
Over the past decades, there has been a rapid expansion in the number of Food Policy Groups (FPG) (including food policy councils, strategies, networks, and informal alliances) operating at municipal and regional levels across North America. FPGs are typically established with the intent of bringing together food systems stakeholders across private (e.g., small businesses, industry associations), public (e.g., government, public health, postsecondary institutions), and community (e.g., non-profits and charitable organizations) sectors to develop participatory governance mechanisms. Recognizing that food systems challenges are too often addressed in isolation, FPGs aim to instill integrated approaches to food related policy, programs, and planning. Despite growing interest, there is little quantitative or mixed methods research about the relationships that constitute FPGs or the degree to which they achieve cross-sectoral integration. Turning to Social Network Analysis (SNA) as an approach for understanding networked organizational relationships, we explore how SNA might contribute to a better understanding of FPGs. This paper presents results from a study of the Thunder Bay and Area Food Strategy (TBAFS), a FPG established in 2007 when an informal network of diverse organizations came together around shared goals of ensuring that municipal policy and governance supported healthy, equitable and sustainable food systems in the Thunder Bay region in Ontario, Canada. Drawing on data from a survey of TBAFS organizational members, we suggest that SNA can improve our understanding of the networks formed by FPGs and enhance their goals of cross-sectoral integration.
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.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".