An Appraisal of Narcissistic Rage Through Path Modeling
Bibliographic record
Abstract
Pathological narcissism and borderline traits have been consistently associated with interpersonal aggression. Shame has been identified as an important trigger of aggressive behaviors in individuals with pathological personality traits, especially for narcissistic vulnerability and borderline traits. This is in line with Kohut's theory on narcissistic rage, that is, aggression, anger, and destruction that act as a protection for a grandiose self. The present study aims to investigate the interrelations between pathological narcissism, borderline traits, shame, and trait aggression, concepts that are parts of the narcissistic rage phenomenon introduced by Kohut, using path models. A total of 399 participants completed self-report questionnaires assessing personality traits (narcissistic grandiosity and vulnerability, and borderline traits), shame, and aggression. Three path models including these variables were tested and compared to one another on fit indices. Results show that shame acts as a mediator between pathological traits (narcissistic vulnerability and borderline traits) and trait aggression, whereas the relationship between narcissistic grandiosity and aggression was direct (i.e., shame was not involved). Results expand the narcissistic rage theory by suggesting that it might represent an internalizing type of aggression that manifests in the context of narcissistic vulnerability and borderline traits, which is not the case for narcissistic grandiosity that exerts a direct effect on trait aggression.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".