Public perceptions of responsibility for recommended food policies in seven countries
Bibliographic record
Abstract
BACKGROUND: Food policy is important to promote healthy and sustainable diets. However, who is responsible for developing and implementing food policy remains contentious. Therefore, this study aimed to investigate how the public attributes responsibility for food policy to governments, individuals and the private sector. METHODS: A total of 7559 respondents from seven countries [Australia (n = 1033), Canada (n = 1079), China (n = 1099), India (n = 1086), New Zealand (n = 1090), the UK (n = 1079) and the USA (n = 1093)] completed an online survey assessing perceived responsibility for 11 recommended food policies. RESULTS: Overall, preferred responsibility for the assessed food policies was primarily attributed to governments (62%), followed by the private sector (49%) and individuals (31%). Respondents from New Zealand expressed the highest support for government responsibility (70%) and those from the USA the lowest (50%). Respondents from the USA and India were most likely to nominate individuals as responsible (both 37%), while those from China were least likely (23%). The private sector had the highest attributed responsibility in New Zealand (55%) and the lowest in China and the USA (both 47%). Support for government responsibility declined with age and was higher among those on higher incomes, with a university degree, and who perceived themselves to consume a healthy diet or be in poor health. CONCLUSIONS: Across seven diverse countries, results indicate the public considers government should take primary responsibility for the assessed food policies, with modest contribution from the private sector and minority support for individual responsibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".