A Scoring Procedure for Malignant Narcissism Based on Personality Inventory for DSM-5 Facets
Bibliographic record
Abstract
The current study focuses on the development and validation of a scoring procedure for malignant narcissism using the Personality Inventory for DSM-5, a self-report measure of Criterion B from the Alternative Model for Personality Disorders. In Study 1, a prototype matching approach was used to aggregate ratings from 15 clinicians specializing in personality disorder treatment and/or assessment. Indices of inter-rater agreement and inter-rater reliability revealed high convergence as to the most important maladaptive facets for malignant narcissism. The scoring procedure, based on additive counts for score computation, included eleven Criterion B facets covering core features of malignant narcissism. Study 2 evaluated the criterion and incremental validity of the scoring procedure in a sample of 288 patients from a personality disorder treatment clinic, as well as in a sample of 1103 participants from the community. In both samples, results from nonparametric mean comparisons, receiver operating characteristic curves, bivariate Pearson correlations, and hierarchical multiple linear regressions showed significant associations between malignant narcissism and broader components of personality functioning, as well as with relevant emotional, relational, and/or behavioral features. This new scoring procedure is a simple and valid method for measuring malignant narcissism, and is suitable for clinical and research settings.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".