Food Policy Council Self-Assessment Tool: Development, Testing, and Results ( FPC-SAT ). Copyright: Public Domain.
Bibliographic record
Abstract
A large number of food policy councils (FPCs) exist in the United States, Canada, and Tribal Nations (N = 278), yet there are no tools designed to measure their members' perceptions of organizational capacity, social capital, and council effectiveness. Without such tools, it is challenging to determine best practices for FPCs and to measure change within and across councils over time. This study describes the development, testing, and findings from the Food Policy Council Self-Assessment Tool (FPC-SAT). The assessment measures council practices and council members' perceptions of the following concepts: leadership, breadth of active membership, council climate, formality of council structure, knowledge sharing, relationships, member empowerment, community context, synergy, and impacts on the food system. All 278 FPCs listed on the Food Policy Network's Online Directory were recruited to complete the FPC-SAT. Internal reliability (Cronbach's α) and inter-rater reliability (AD, rWG(J), ICC [intraclass correlations][1], ICC[2]) were calculated, and exploratory and a confirmatory factor analyses were conducted. Responses from 354 FPC members from 94 councils were used to test the assessment. Cronbach's α ranged from 0.79 to 0.93 for the scales. FPC members reported the lowest mean scores on the breadth of active membership scale (2.49; standard deviation [SD], 0.62), indicating room for improvement, and highest on the leadership scale (3.45; SD, 0.45). The valid FPC-SAT can be used to identify FPC strengths and areas for improvement, measure differences across FPCs, and measure change in FPCs over time.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".