Food safety, voluntary recall and firm reputation
Bibliographic record
Abstract
Abstract Product recalls are direct remedies for producers in case of food safety problems. Unlike in the United States or other developed countries, in China, voluntary recalls are rarely documented in the food sector. The purpose of this paper is to address two questions: (1) why do firms adopt different recall strategies in different countries; (2) under what circumstances can voluntary recall help firms build up their food safety reputation? Based on the theory of collective reputation, we develop a dynamic model to incorporate firms' recall strategy and investigate the impact of such a strategy on industry collective reputation. The model takes into account production hazards: producers' lapses in food safety despite good‐faith efforts. Our results show that voluntary recall helps a firm to maintain a good historical record. Hence, firms are likely to achieve a high level of collective reputation under voluntary recall. However, a firm is willing to initiate voluntary recall only if the collective reputation is high enough. This explains the current situation in China: as consumers show little trust (belief in collective reputation is low), firms cannot recover the recall loss and thus have no incentive to initiate a voluntary recall.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".