Perfectionism paradox: Perfectionistic concerns (not perfectionistic strivings) affect the relationship between perceived risk and choice
Bibliographic record
Abstract
Abstract We investigate whether, when, and why perfectionism moderates the relationship between perceived risk and choice. Two studies ( N = 1784) using different choice domains (appearance and performance) and different samples (women and general population) show consistent results. People with high (vs. low) perfectionistic concerns (PC) are less sensitive to high risks and, hence, are more willing to consider options (i.e., products and services) that entail greater risks. These effects emerge because high‐PC (vs. low‐PC) individuals have more favorable appraisals, believing that the product or service's benefits are worth its risks even when these risks are substantial. The effects observed for high‐ vs. low‐PC do not obtain for people who are high (vs. low) on a second dimension of perfectionism called perfectionistic strivings (PS). Our findings suggest that high‐PC individuals may be a vulnerable segment in society, particularly since (a) people are frequently confronted with decisions about options that promise perfectionistic outcomes, (b) these options can come with high levels of risk, and (c) perfectionistic tendencies have become more prevalent over time. We discuss the implications of these findings for policymakers and future research.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".