I'm Too Good for This Job: Narcissism's Role in the Experience of Overqualification
Bibliographic record
Abstract
Using relative deprivation theory, we examined the role of narcissism in moderating the relationships between objective overqualification and perceived overqualification, job satisfaction, and career‐related work stress. Permanently employed participants ( N = 292) completed an online survey, which included measures of narcissism, overqualification, and job attitudes. The exploitiveness/entitlement subscale of narcissism was positively associated with perceived overqualification, though only modestly ( r = .13). Both exploitiveness/entitlement and perceived overqualification were associated with lower job satisfaction and higher career‐related work stress. Hierarchical multiple regression analyses revealed that, unlike non‐narcissistic employees, employees scoring high on exploitiveness/entitlement reported feeling overqualified even when they did not possess surplus education relative to job requirements. Surprisingly, while objective overqualification was positively associated with work stress for non‐entitled employees, highly entitled employees did not experience greater work stress when objectively overqualified. We explore possible explanations for this finding, and outline future directions for research on narcissism and overqualification.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".