Cross-country comparison of strategies for building consumer trust in food
Bibliographic record
Abstract
Consumer trust in the modern food system is essential given its complexity. Contexts vary across countries with regard to food incidents, regulation and systems. It is therefore of interest to compare how key actors in different countries might approach (re)building consumer trust in the food system; and particularly relevant to understanding how food systems in different regions might learn from one another. The purpose of this paper is to explore differences between strategies for (re)building trust in food systems, as identified in two separate empirical studies, one conducted in Australia, New Zealand and the UK (Study 1) and another on the Island of Ireland (Study 2). Interviews were conducted with media, food industry and food regulatory actors across the two studies (n = 105 Study 1; n = 50 Study 2). Data were coded into strategy statements, strategies describing actions to (re)build consumer trust. Strategy statements were compared between Studies 1 and 2 and similarities and differences were noted. The strategy statements identified in Study 1 to (re)build consumer trust in the food system were shown to be applicable in Study 2, however, there were notable differences in the contextual factors that shaped the means by which strategies were implemented. As such, the transfer of such approaches across regions is not an appropriate means to addressing breaches in consumer trust. Notwithstanding, our data suggest that there is still capacity to learn between countries when considering strategies for (re)building trust in the food system but caution must be exercised in the transfer of approaches.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".