Exploring the intersectoral partnerships guiding Australia's dietary advice
Bibliographic record
Abstract
In 1986, the Ottawa Charter alerted a new generation of health promotion practitioners to the benefits of working with the non-health sectors, including the commercial sector. Since then, the establishment of partnerships with government and non-government bodies has been advanced as a positive way of fostering policies that enhance health and well-being. The food and nutrition field has enthusiastically adopted partnerships between government, non-government and industry. In this article, we focus on the tactics employed by industry bodies to further their cause in a range of fields that are characterized by risk and contestation. We describe the nature of the alliances and interactions between commercial, scientific and government groups whose stated aim is to improve Australia's diet. Our analysis shows that these partnerships have been guided less by the ethos of the Ottawa Charter and more by the interests of the various parties: namely the food industry's need for credibility in making health claims, the financial imperatives of professional bodies and scientists whose public funding is inadequate, and government endorsement of public-private partnerships as the preferred mechanism for service delivery. The symbiotic relationship that is emerging between segments of the food industry and the nutrition professions raises questions about the independence of the dietary advice being given to consumers. We conclude by arguing for a research programme to investigate the consequences of intersectoral partnerships on the nutritional status of the population.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".