The Anti-Mattering Scale Versus the General Mattering Scale in Pathological Narcissism: How an Excessive Need to Matter Informs the Narcissism and Mattering Constructs
Bibliographic record
Abstract
The current study uniquely examines mattering and narcissism and reflects our contention that pathological narcissism involves an excessive need to matter and a hypersensitivity to being devalued and not mattering to other people. Specifically, we evaluated the proposed association between pathological narcissism and deficits in mattering by comparing the results obtained with the Anti-Mattering Scale and the General Mattering Scale. We also evaluated anti-mattering as a potential mediator of the link between narcissism and distress. A sample of 168 university students completed the Anti-Mattering Scale, the General Mattering Scale, the Pathological Narcissism Inventory, and a depression measure. Results confirmed that elevated scores on the Anti-Mattering Scale are associated with grandiose and vulnerable narcissism as well as depression. Mattering assessed by the General Mattering Scale had a weaker association with narcissism, thus highlighting the distinction between the Anti-Mattering Scale and the General Mattering Scale. Further analyses suggested that elevated Anti-Mattering Scale scores did indeed mediate the link between vulnerable narcissism and depression in keeping with anti-mattering as a factor that elicits the vulnerability of narcissists. Our findings attest to the uniqueness of the Anti-Mattering Scale and illustrate the need to consider the role of feelings of not mattering as a contributor to the self and identity issues and interpersonal sensitivity that contribute to pathological narcissism. This work also suggests the need to emphasize an excessive need to matter when assessing the self and when developing future measures of the need to matter.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".