Building food safety into the company culture: a look at Maple Leaf Foods
Bibliographic record
Abstract
Maple Leaf Foods learned a hard lesson following its tragic 2008 Listeria outbreak that ended up taking the lives of 23 Canadians. The organization has since 2008 transformed its commitment to food safety with a strong drive and manifest in embedding sustainable food safety behaviours into the existing company culture. Its focus on combining technical risk analysis with behavioural sciences has led to the development and deployment of a food safety strategy deeply rooted in the company values and management commitment. Using five tactics described in this article the organization has been on a journey towards food safety transformation through adoption of best practices for people and systems. The approach to food safety has been one where food safety is treated as a non-competitive issue and Maple Leaf Foods have been open to sharing learning about what happened and how the organization will continue to take a leadership position in food safety to continuously raise the bar for food safety across the industry. Maple Leaf Foods has benefited tremendously by learning about best practice from numerous companies in North America and around the world. The authors believe this brief story will bring value to others as we continue to learn and improve.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".