Food safety, voluntary recall and firm reputation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it