Crying Wolf or Ever Vigilant: Do Wide-Ranging Product Warnings Increase or Decrease Sensitivity to Other Product Warnings?
Bibliographic record
Abstract
Product safety warnings are pervasive in the marketplace. The frequency and, in some cases, content of such warnings has led some to speculate that the cumulative effects may undermine the efficacy of warnings in general—including that of different warnings for other products. According to the generalized desensitization hypothesis, numerous past warnings can cause consumers to react less strongly to safety warnings for other products subsequently encountered. In contrast, the literature on goal activation and compensatory consumer behavior suggests that any self-protective goals aroused by initial warnings can potentially generalize to increase awareness and safety precautions in other warning contexts, consistent with the generalized sensitization hypothesis. The authors tested both hypotheses by manipulating the number and strength of an initial set of product warnings and examining whether such exposure generalized to different product warnings. In support of the generalized sensitization prediction, prior warnings motivated appreciation of the risks communicated in a different warning context and increased relevant safety behaviors. These generalized sensitization effects were moderated by self-affirmation, supporting the prediction that they are driven by self-protective goals.
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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".