Expectations and everyday opportunities for building trust in the food system
Bibliographic record
Abstract
Purpose Consumers’ trust in food systems is essential to their functioning and to consumers’ well-being. However, the literature exploring how food safety incidents impact consumer trust is theoretically underdeveloped. This study explores the relationship between consumers’ expectations of the food system and its actors (regulators, food industry and the media) and how these influence trust-related judgements that consumers make during a food safety incident. Design/methodology/approach In this study, two groups of purposefully sampled Australian participants (n = 15) spent one day engaged in qualitative public deliberation to discuss unfolding food incident scenarios. Group discussion was audio recorded and transcribed for the analysis. Facilitated group discussion included participants' expected behaviour in response to the scenario and their perceptions of actors' actions described within the scenario, particularly their trust responses (an increase, decrease or no change in their trust in the food system) and justification for these. Findings The findings of the study indicated that food incident features and unique consumer characteristics, particularly their expectations of the food system, interacted to form each participant's individual trust response to the scenario. Consumer expectations were delineated into “fundamental” and “anticipatory” expectations. Whether fundamental and anticipatory expectations were in alignment was central to the trust response. Experiences with the food system and its actors during business as usual contributed to forming anticipatory expectations. Originality/value To ensure that food incidents do not undermine consumer trust in food systems, food system actors must not only demonstrate competent management of the incident but also prioritise trustworthiness during business as usual to ensure that anticipatory expectations held by consumers are positive.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".