Conducting fit‐for‐purpose food safety risk assessments
Bibliographic record
Abstract
The interplay between science, risk assessment and risk management has always been complex, and even more so in a world increasingly characterised by rapid technical innovation, new modes of communication, suspicion about authorities and experts, and demands for people to have a say in decisions that are made on their behalf. In this challenging era where scientific advice on food safety has never been in greater demand, risk managers should effectively navigate the interplay between facts and values and be able to rely on robust and fit-for-purpose risk assessments to aid them. The fact that societal resistance is often encountered when scientific advice on food safety operates at a distance from social values and fails to actively engage with citizens, has led to increasing emphasis on the need to advance forms of risk assessment that are more contextual, and socially sound and accountable. EFSA's third Scientific Conference explored how risk assessments could be constructed to most usefully meet society's needs and thus connect science with society, while remaining scientifically robust. Contributors to the conference highlighted the need to: (1) frame risk assessments by clear policy goals and decision-making criteria; (2) begin risk assessments with an explicit problem formulation to identify relevant information; (3) make use of reliable risk assessment studies; (4) be explicit about value judgements; (5) address and communicate scientific uncertainty; (6) follow trustworthy processes; (7) publish the evidence and data, and report the way in which they are used in a transparent manner; (8) ensure effective communication throughout the risk analysis process; (9) involve society, as appropriate; and (10) weigh risks and benefits on request. Implementation of these recommendations would contribute to increased credibility and trustworthiness of food safety risk assessments.
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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 | 0.247 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".