Categorization of drivers of change for emerging food safety risks
Bibliographic record
Abstract
The emergence of food safety risks can be influenced by various causes, also known as drivers of change. Understanding the characteristics related to the manageability of these drivers and the health impact of their associated hazards is critical for effective food safety management and resource allocation. This study aims to categorize drivers of change for known food safety hazards based on their impact on human health and their manageability. Identified drivers were categorized and ranked through an expert survey and a PROMETHEE multi-criteria analysis. The weighted performance criteria are controllability and volatility of the driver, and likelihood and severity of the associated hazards. Results show that the severity of associated hazards is the most important criterion, while volatility of the driver is considered least important by experts. All drivers of change were ranked based on their potential impact on food safety and categorized in a driver matrix with four categories. Categorization is based on the combined effect of expected negative health impact (low or high) and manageability (difficult or easy). The four categories are: "monitor and adapt" (low, difficult), "analyze and optimize" (low, easy), "leverage and innovate" (high, easy), and finally "strategize and endure" (high, difficult). Two drivers-environmental contamination and geopolitical conflict-are categorized as difficult to manage and associated with highly likely and severe health consequences. These drivers represent critical challenges requiring long-term strategic efforts. Six drivers are highly manageable (low health impact: legislation, policies and governance, technologies in food production, food processing technologies; high impact: management of natural resources, bioprocesses, supply chain) and could be leveraged to influence food safety hazards. The results are shown to be robust against changes in criteria weighting. These findings can support decision makers in shifting focus toward manageable drivers and tailoring strategies by driver category. For effective action, it is essential to understand the specific risks and benefits of each driver and the burden of their associated hazards. Future work should explore the top-ranked drivers and integrate these insights into holistic food safety management strategies.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".