A model for (re)building consumer trust in the food system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it