Development of the Local Food Systems Policy Index (Local Food-EPI+) tool and assessment process to benchmark the implementation of local government policies for creating healthy, equitable and environmentally sustainable food systems
Bibliographic record
Abstract
Abstract Objective: Local governments have an important role to play in creating healthy, equitable and environmentally sustainable food systems. This study aimed to develop and pilot a tool and process for local governments in Australia to benchmark their policies for creating healthy, equitable and environmentally sustainable food systems. Design: The Healthy Food Environment Policy Index (Food-EPI), developed in 2013 for national governments, was tailored to develop the Local Food Systems Policy Index (Local Food-EPI+) tool for local governments. To incorporate environmental sustainability and the local government context, this process involved a literature review and collaboration with an international and domestic expert advisory committee (n 35) and local government officials. Setting: Local governments. Results: The tool consists of sixty-one indicators across ten food policy domains (weighted based on relative importance): leadership; governance; funding and resources; monitoring and intelligence; food production and supply chain; food promotion; food provision and retail in public facilities and spaces; supermarkets and food sources in the community; food waste reuse, redistribution and reduction; and support for communities. Pilot implementation of the tool in one local government demonstrated that the assessment process was feasible and likely to be helpful in guiding policy implementation. Conclusion: The Local Food-EPI+ tool and assessment process offer a comprehensive mechanism to assist local governments in benchmarking their actions to improve the healthiness, equity and environmental sustainability of food systems and prioritise action areas. Broad use of this tool will identify and promote leading practices, increase accountability for action and build capacity and collaborations.
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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 | 0.121 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".