Misperceiving grandiose narcissism as self‐esteem: Why narcissists are well liked at zero acquaintance
Bibliographic record
Abstract
OBJECTIVE: We examine why people form positive first impressions of grandiose narcissists, even though they can identify others' narcissism. We test whether this occurs because narcissists are perceived to have especially high self-esteem, which is socially valued. METHOD: Across four studies, undergraduate perceivers viewed photographs of targets (for whom narcissism and self-esteem were known) and rated perceptions of their narcissism and self-esteem, as well as how much they liked them. RESULTS: Perceivers rated more narcissistic targets to be higher in self-esteem (even compared to targets with equally high self-esteem) and liked them more. Perceptions of self-esteem, moreover, mediated the effect of target narcissism on liking (Study 1). This effect disappeared when targets' narcissism was made salient, suggesting that trait narcissism is not inherently attractive (Study 2). Finally, path models revealed a negative effect of perceptions of narcissism on liking that was suppressed by a positive effect of perceptions of self-esteem on liking (Study 3a), even for ratings of people's online dating profiles (Study 3b). CONCLUSIONS: Positive initial impressions of narcissists may be driven by inflated perceptions that they have high self-esteem.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".