What Is (and Isn’t) a Product Recall?
Bibliographic record
Abstract
Safety in consumer goods is maintained by product safety laws and associated regulations. However, the legislation and regulations are specific to product categories and legal jurisdictions, thus impeding one's ability to understand what a recall is and isn’t, and how it differs from related phenomena (e.g., product-harm crisis). The authors aim to provide such an understanding. They reviewed 510 reports from academics, managers, governments, and regulators; conducted interviews with 25 practitioners; and used 10 recall data sets to identify seven fundaments of recall. They synthesize the fundaments to propose a definition and a decision tree of recall, which can help inform academics, journalists, managers, lawyers, and safety advocates regarding what term is appropriate in what context. The authors apply the fundaments to identify similarities and differences between a recall and a harm crisis, the term used frequently in marketing research in association with recall. The fundaments also enable the authors to make five recommendations each for lawmakers and regulators in an effort to guide the academic and practitioner discourse on product 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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".