Perfectionism paradox: Perfectionistic concerns (not perfectionistic strivings) affect the relationship between perceived risk and choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it