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.
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 | 0.038 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".