Testing the limits of optimistic bias: Event and person moderators in a multilevel framework.
Bibliographic record
Abstract
N. D. Weinstein (1980) established that optimistic bias, the tendency to see others as more vulnerable to risks than the self, varies across types of event. Subsequently, researchers have documented that this phenomenon, also known as comparative optimism, also varies across types of people. The authors integrate hypotheses originally advanced by Weinstein concerning event-characteristic moderators with later arguments that such optimism may be restricted to certain subgroups. Using multilevel modeling over 7 samples (N = 1,436), the authors found that some degree of comparative optimism was present for virtually all individuals and events. Holding other variables constant, higher perceived frequency and severity were associated with less comparative optimism, higher perceived controllability and stereotype salience with more comparative optimism. Frequency, controllability, and severity were associated more with self-risk than with average-other risk, whereas stereotype salience was associated more with average-other risk than with self-risk. Individual differences also mattered: comparative optimism was related negatively to anxiety and positively to defensiveness and self-esteem. Interaction results imply that both individual differences and event characteristics should jointly be considered in understanding optimistic bias (or comparative optimism) and its application to risk communication.
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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 | 0.043 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".