Comment minimiser les effets négatifs des rappels de produits?
Bibliographic record
Abstract
Résumé Devant le nombre grandissant de rappels de produits et la surmédiatisation de ces derniers, il devient essentiel pour les entreprises de savoir gérer ce type de crise afin d’en limiter les impacts à court terme et d’assurer la pérennité à long terme de la marque et de l’entreprise. Les rappels de produits ont des coûts directs importants dus aux défis logistiques, aux pertes de clients et de revenus, à l’indemnisation des consommateurs ou aux amendes imposées. Ils ont aussi des coûts indirects liés aux perceptions négatives des consommateurs à l’égard de l’entreprise et donc à la détérioration de l’image de marque, du capital de marque et de la réputation. Cet article décrit de nombreux facteurs susceptibles de minimiser les effets négatifs des rappels de produits.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".