A Cold Cut Crisis: Listeriosis, Maple Leaf Foods, and the Politics of Apology
Bibliographic record
Abstract
In the summer of 2008, one of the worst cases of food contamination in Canadian history was confirmed when the Canadian Food Inspection Agency and Maple Leaf Foods issued a “health hazard alert” warning the public not to serve or consumer Sure Slice brand cold cuts. This localized warning quickly spiralled into a major listeriosis epidemic. More than 200 Maple Leaf Foods products were recalled, but not in time to prevent 20 deaths, the illness of thousands more and a class action lawsuit. This article explores Maple Leaf’s crisis response strategy. Locating our analysis in relation to theorizing about the legitimacy problems that corporations and other powerful actors face in late modernity, it demonstrates that Maple Leaf’s apology was effective in terms of restoring consumer trust and confidence to the extent that it addressed the uncertainties and anxieties that are endemic to contemporary risk society; and, more broadly, it ‘worked’ by disrupting the distribution of risk and blame to other stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.046 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".