Food-system actors’ perspectives on trust: an international comparison
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare the perspectives of actors who contribute to trust in the food system in four high income countries which have diverse food incident histories: Australia, New Zealand (NZ), the United Kingdom (UK) and the Island of Ireland (IOI), focussing on their communication with the public, and their approach to food system interrelationships. Design/methodology/approach Data were collected in two separate studies: the first in Australia, NZ and the UK (Study 1); and the second on the IOI (Study 2). In-depth interviews were conducted with media, food industry and food regulatory actors across the four regions ( n =105, Study 1; n =50, Study 2). Analysis focussed on identifying similarities and differences in the perspectives of actors from the four regions regarding the key themes of communication with the public, and relationships between media, industry and regulators. Findings While there were many similarities in the way food system actors from the four regions discussed (re)building trust in the context of a food incident, their perceptions differed in a number of critical ways regarding food system actor use of social media, and the attitudes and approaches towards relationships between food system actors. Originality/value This paper outlines opportunities for the regions studied to learn from each other when looking for practical strategies to maximise consumer trust in the food system, particularly relating to the use of social media and attitudes towards role definition in industry–regulator relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".