Management of food incidents by<scp>A</scp>ustralian food regulators
Bibliographic record
Abstract
Abstract Aim This paper explores how food regulators respond to food incidents and the barriers and enablers associated with doing so. Methods Twenty‐six semi‐structured interviews lasting between 30 and 60 minutes were undertaken with Australian food regulators. Regulators worked across food policy development, implementation, enforcement and standards setting. These interviews ascertained food regulators' views on food safety and responses to real and hypothetical food incidents. Data were analysed using thematic analysis. Results Food regulators reported that working together with other food regulators is an important part of effective food regulation and response to food incidents. Strategies for working together included clarifying expectations and developing formal documents such as a memorandum of understanding. However, challenges in working together were reported, including different risk thresholds, different political agendas and a lack of clarity on regulators' roles. Conclusions A focus on partnerships and good communication between food regulators is likely to facilitate effective management of food incidents, and maximise the chances that food incidents do not lead to increased consumer morbidity and mortality as a result of a poor response to a food incident.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".