Food fairness Illawarra: Factors enabling an effective coalition to ensure a fair food future
Bibliographic record
Abstract
Food security, access to appropriate, nutritious food on a regular, reliable basis, is a human right and core to Australia’s future. However, it is increasingly recognised that groups in the Australian community are food insecure, including >6% of Illawarra residents. In recognition of this, Food Fairness Illawarra formed as a community alliance to promote a fair food future for residents. Collaborative community partnerships and coalitions are a core Ottawa Charter strategy for enhancing health. It is important therefore to evaluate the effectiveness of such coalitions in promoting community food security. The effectiveness of Food Fairness Illawarra as a community coalition to enhance food security can be measured in terms of outcomes and processes. The coalition has been successful in a wide range of outcomes at a number of strategic levels to promote and enhance local food security. In addition, the coalition has surveyed members regularly concerning satisfaction, communication and capacity building to ensure that the coalition has appropriate processes for continuing effectiveness. This discussion will present the outcome and process measures used by Food Fairness Illawarra to demonstrate its effectiveness and highlight the factors contributing to the success of this community coalition in promoting community food security.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".