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.
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 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 | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".