A model for (re)building consumer trust in the food system
Bibliographic record
Abstract
The article presents a best practice model that can be utilized by food system actors to assist with (re)building trust in the food system, before, during and after a food incident defined as 'any situation within the food supply chain where there is a risk or potential risk of illness or confirmed illness or injury associated with the consumption of a food or foods' (Commonwealth of Australia. National Food Incident Response Protocol. Commonwealth of Australia, Canberra, 2012). Interviews were undertaken with 105 actors working within the media, food industry and food regulatory settings across Australia, New Zealand (NZ) and the United Kingdom (UK). Interview data produced strategy statements, which indicated participant views on how to (re)build consumer trust in the food system. These included: (i) be transparent, (ii) have protocols and procedures in place, (iii) be credible, (iv) be proactive, (v) put consumers first, (vi) collaborate with stakeholders, (vii) be consistent, (viii) educate stakeholders and consumers, (ix) build your reputation and (x) keep your promises. A survey was designed to enable participants to indicate their agreement/disagreement with the ideas, rate their importance and provide further comment. The five strategies considered key to (re)building consumer trust were used to develop a model demonstrating best practice strategies for (re)building consumer trust in the food system before, during and after a food incident. In a world where the food system is increasingly complex, strategies for (re)building and fostering consumer trust are important. This study offers a model to do so which is derived from the views and experiences of actors working across the food industry, food regulation and the media.
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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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".