Burning With Envy? Dispositional and Situational Influences on Envy in Grandiose and Vulnerable Narcissism
Bibliographic record
Abstract
Research on narcissism and envy suggests a variable relationship that may reflect differences between how vulnerable and grandiose narcissism relate to precursors of envy. Accordingly, we proposed a model in which dispositional envy and relative deprivation differentially mediate envy's association with narcissistic vulnerability, grandiosity, and entitlement. To test the model, 330 young adults completed dispositional measures of narcissism, entitlement, and envy; one week later, participants reported on deprivation and envy feelings toward a peer who outperformed others on an intelligence test for a cash prize (Study 1) or earned higher monetary payouts in a betting game (Study 2). In both studies, structural equation modeling broadly supported the proposed model. Vulnerable narcissism robustly predicted episodic envy via dispositional envy. Entitlement-a narcissistic facet common to grandiosity and vulnerability-was a significant indirect predictor via relative deprivation. Study 2 also found that (a) the grandiose leadership/authority facet indirectly curbed envy feelings via dispositional envy, and (b) episodic envy contributed to schadenfreude feelings, which promoted efforts to sabotage a successful rival. Whereas vulnerable narcissists appear dispositionally envy-prone, grandiose narcissists may be dispositionally protected. Both, however, are susceptible to envy through entitlement when relative deprivation is encountered.
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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 | 0.004 |
| 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 | 0.001 |
| 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".