Shaping Effective Food Action Groups: Participant Perspectives on Structure and Stakeholder Involvement in Regional and Remote Western Australia
Bibliographic record
Abstract
ISSUE ADDRESSED: Food systems strongly influence food security outcomes. Food Action Groups (often termed Food Policy Councils/Coalitions/Networks internationally) offer a co-ordinated and collaborative approach to local food system issues. Their organisational structure and stakeholder membership significantly impact their focus and impact. Therefore, it is imperative to understanding community member and food system stakeholders' perspectives on how regional and remote Australian Food Action Groups should be structured to maximise their impact on local food systems, and identify the most appropriate stakeholders to facilitate and drive their action. METHODS: A qualitative study using focus groups, was conducted in regional and remote townships across Western Australian regions of Peel, South West, Great Southern, Wheatbelt, Midwest (including Gascoyne), Goldfields, Pilbara, and Kimberley. Participants were community members and food system stakeholders. Focus group transcript data were thematically analysed. RESULTS: A formal structure with sustainable funding was important for Food Action Groups, as was adopting a bottom-up approach with local community needs driving the agenda, supported by an adaptable and responsive work plan. Involving community members and ensuring a diverse membership were viewed as critical to their success. CONCLUSIONS: To effectively address local needs, Food Action Groups should adopt a formal structure with clear processes and involve a diverse group of community stakeholders. This would leverage local knowledge and evidence to guide actions and set well-informed priorities. SO WHAT: The establishment of Australian Food Action Groups in regional and remote Australia has potential to follow suit of their US, Canada and UK predecessors, improve regional food systems and influence government policies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".