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
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.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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