Bibliographic record
Abstract
OBJECTIVE: Though initially charming and inviting, narcissists often engage in negative interpersonal behaviors. Identifying and avoiding narcissists therefore carries adaptive value. Whereas past research has found that people can judge others' grandiose narcissism from their appearance (including their faces), the cues supporting these judgments require further elucidation. Here, we investigated which facial features underlie perceptions of grandiose narcissism and how they convey that information. METHOD AND RESULTS: In Study 1, we explored the face's features using a variety of manipulations, ultimately finding that accurate judgments of grandiose narcissism particularly depend on a person's eyebrows. In Studies 2A-2C, we identified eyebrow distinctiveness (e.g., thickness, density) as the primary characteristic supporting these judgments. Finally, we confirmed the eyebrows' importance in Studies 3A and 3B by measuring how much perceptions of narcissism changed when swapping narcissists' and non-narcissists' eyebrows between faces. CONCLUSIONS: Together, these data show that distinctive eyebrows reveal narcissists' personality to others, providing a basic understanding of the mechanism through which people can identify narcissistic personality traits with potential application to daily life.
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 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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".