How food regulators communicate with consumers about food safety
Bibliographic record
Abstract
Purpose – The purpose of this paper is to report how food regulators communicate with consumers about food safety and how they believe consumers understand their role in relation to food safety. The implications of this on the role of food regulators are considered. Design/methodology/approach – In total, 42 food regulators from Australia, New Zealand and the UK participated in a semi-structured interview about their response to food incidents and issues of food regulation more generally. Data were analysed thematically. Findings – Food regulators have a key role in communicating information to consumers about food safety and food incidents. This is done in two main ways: proactive and reactive communication. The majority of regulators said that consumers do not have a good understanding of what food regulation involves and there were varied views on whether or not this is important. Practical implications – Both reactive and proactive communication with consumers are important, however there are clear benefits in food regulators communicating proactively with consumers, including a greater understanding of the regulators’ role. Regulators should be supported to communicate proactively where possible. Originality/value – There is a lack of information about how food regulators communicate with consumers about food safety and how food regulators perceive consumers to understand food regulation. It is this gap that forms the basis of this paper.
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.
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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".