Institutional and relational determinants in high- and medium-extent food product crises: the inner perspective of a public health crisis
Bibliographic record
Abstract
In 2008, Canada enacted its biggest-ever food recall in response to a Listeria crisis, stemming from a Maple Leaf Foods plant, that killed 22 Canadians. Afterwards, Maple Leaf's market share quickly returned to pre-crisis levels, but the long-term repercussions of the scare still reverberate in Maple Leaf's brand. In this case study, which offers an organizational perspective on the food recall, data was collected, through in-depth interviews of persons involved in the crisis response, and analyzed. The aim of this paper is to make transparent the ways in which Maple Leaf Foods organized their resources to manage the 2008 food recall. Results reveal that institutional and relational determinants are the most important factors in high- and medium-extent food product crises, whereas external and internal effects primarily influence an organization's capacity to cope with severe crises. Based on these findings, a conceptual framework is presented and managerial implications are discussed.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".