Public involvement and risk communication in food safety governance: lessons from listeria monocytogenes and vulnerable groups
Bibliographic record
Abstract
With a primary focus on Health Canada (HC) and the Canadian Food Inspection Agency (CFIA), this thesis describes the state of microbial related public involvement and risk communication undertakings. The findings show that HC engages with experts to a far greater extent than with the lay public and that HC has not upheld its stated commitment to transparency. Furthermore, both HC’s and the CFIA´s approach to risk communication is overly general, has failed to provide opportunities for dialogue with vulnerable groups and is not rooted in foodborne surveillance data. Public involvement in food safety governance would be improved if HC provided the lay public with a seat on advisory committees and improved its reporting methods. HC and the CFIA could also make improvements by creating opportunities for dialogue between officials and the general public, and by exploring the potential use of alternative risk communication vehicles, such as food labels.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| 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".