An examination of the foodsafety (FSnet), agriculture (Agnet) and animal networks (Animalnet) as tools in food safety risk analysis
Bibliographic record
Abstract
Risk analysis--the integration of assessment, management and communication--has been used by government, academia, food industry, and consumers, to develop food safety risk policies. The internationally distributed listservs, the Food Safety, Agriculture and Animal Networks, produced at the University of Guelph were developed to aid in the interactive communication amongst stakeholders demanded by an integrative risk analysis model. This thesis attempted to determine whether the information in the listservs was being used to address food safety risk issues that appear in the media and to identify differences in the risk perceptions held by the groups noted above. Although the attitudes towards food safety issues were found similar, the terms used by the public, government, industry and academia may have different interpretations. A better understanding of the players involved in risk analysis can lead to more effective risk management and risk communication strategies and, ultimately, more responsive public policies.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".