Five‐factor model trait profiles of narcissistic grandiosity and narcissistic vulnerability in Iranian university students
Bibliographic record
Abstract
This study examined the relationship between pathological narcissism, narcissistic grandiosity, narcissistic vulnerability and the five-factor model of personality. Participants consisted of 290 undergraduate students from four universities in three different cities in Iran, recruited by available sampling, Instruments, including, Pathological Narcissism Inventory (PNI) and the NEO Five-Factor Inventory (NEO-FFI) were also completed for the participants. Hierarchical regression analysis showed that narcissistic grandiosity was positively associated with extraversion and openness, while narcissistic vulnerability and overall pathological narcissism were positively associated with neuroticism and negatively related to agreeableness and openness (only for narcissistic vulnerability). The results are consistent with prior research in Western cultures (e.g., United States, Germany) and revealed that neuroticism is a common factor in narcissistic vulnerability and pathological narcissism which suggested pathological narcissism may be a distinct dimension from normal narcissism. Also, there were various contributors of personality traits for narcissistic grandiosity and narcissistic vulnerability which can be considered as a support for the distinction of two phenotypes of pathological narcissism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".