Competing public narratives in nutrition policy: insights into the ideational barriers of public support for regulatory nutrition measures
Bibliographic record
Abstract
Abstract Background Enacting evidence-based public health policy can be challenging. One factor contributing to this challenge is a lack of public support for specific policies, which may stem from limited interest or conviction by policy arguments. This can happen when messaging strategies regarding policy do not resonate with the target group and/or policy narratives compete in public discourse. To understand how policy messaging can better resonate with a target audience, we examined the frames and narratives used by the Australian public when discussing nutrition policies. Methods We conducted 76 street intercept interviews in urban and regional settings in Queensland, Australia. Quantitative data were analysed using mean agreement scores and t-tests, and the qualitative data were analysed using an adapted qualitative narrative policy framework (QNPF). The QNPF is used to illustrate how competing narratives vary in the way they define different elements. These elements often include setting, characters, plot, policy solution and belief systems. Results Level of support for all nutrition policies was generally moderate to high, although nutrition policies perceived to be most intrusive to personal freedoms were the least popular among the public. The value of fairness was consistently invoked when participants discussed their support for or opposition to policy. Using the QNPF, two distinct settings were evident in the narratives: concern for the community or concern for self. Villains were identified as either “other individuals, in particular parents” or “Big Food”. Victims were identified as “children” or “the food industry, in particular farmers”. Frequently used plots focused on individuals making poor choices because they were uneducated, versus Big Food being powerful and controlling people and the government. Conclusions The study examined the frames and narratives used by the Australian public when discussing nutrition policies. By examining these frames and narratives, we gained insight into multiple strategies which may increase public support for certain nutrition policies in Australia.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".