How self‐esteem and narcissism differentially relate to high and (un)stable feelings of status and inclusion
Bibliographic record
Abstract
OBJECTIVE: We tested how self-esteem and grandiose narcissism are associated with people's level and instability of status and inclusion. METHOD: In Studies 1 and 2, we used latent profile analysis (Study 1, N = 989; Study 2, N = 470, 111 teams) to examine how people felt about their level and instability of status and inclusion. In Study 3, we used daily diary reports (N = 287, 1,286 daily observations) to track people's level and instability of status and inclusion. RESULTS: Higher levels of status and inclusion did not always correspond to more stable beliefs about one's social standing. Self-esteem predicted higher and more stable feelings of status and inclusion. Although narcissistic admiration also predicted higher levels of status and inclusion, we found mixed evidence regarding its link to the instability of such feelings. Narcissistic rivalry, however, predicted more unstable feelings of status and inclusion. CONCLUSIONS: By modeling the heterogeneity of status and inclusion feelings across subgroups (Studies 1 and 2) and documenting the degree of instability people experience regarding such feelings (Study 3), these results provide insight into how self-esteem and narcissism relate to the level and instability of status and inclusion.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".