Evaluating Food Policy Councils Using Structural Equation Modeling
Bibliographic record
Abstract
Abstract At least 282 Food Policy Councils ( FPC s) are currently working to improve access to healthy foods in their communities by connecting food system sectors, gathering community input, and advising food policy. Empirical research on FPC s is limited. This study empirically evaluates FPC s to better understand the relationships between Organizational Capacity, Social Capital, and Council Effectiveness by testing a FPC Framework adapted from Allen and colleagues (2012). Members of all FPC s in the U.S., Canada, and Native American Tribes and First Nations were invited to complete the Food Policy Council Self‐Assessment Tool ( FPC ‐ SAT ). Structural equation modeling was used to test the FPC Framework. Three hundred and fifty‐four FPC members from 95 councils completed the FPC ‐ SAT . After slight modification, a revised FPC Framework was a good fit with the data (χ 2 = 40.085, df = 24, p ‐value = .021, comparative fit index = 0.988, Tucker Lewis index = 0.982, root mean squared error of approximation = 0.044, p ‐close = .650). A moderation analysis revealed that community context influences the relationship between Social Capital and Council Effectiveness within the FPC Framework. The FPC Framework can guide capacity building interventions and FPC evaluations. The empirically tested framework can help FPC s efficiently work toward achieving their missions and improving their local food system.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".