Superheroes vs. Saints: The A&C Model of Narcissism, Needs, and Leadership Behaviors
Bibliographic record
Abstract
Approaching the narcissism-leadership relationship from a multi-faceted perspective has been suggested as a next step in understanding this fascinating relationship. To date, leadership research has primarily focused on agentic narcissism. However, studying leadership and narcissism from an agency-communion perspective allows for a deeper understanding of their relationship and more nuanced inferences surrounding leadership behaviors. To illustrate, agentic narcissism is centered around inflated self-views in agentic domains (e.g., intelligence, competence, and achievement; Gebauer et al. 2012), which mirrors an egoistic bias (Paulhus & John, 1998). On the other hand, communal narcissism centers around inflated self-views in communal domains (e.g., trustworthiness, warmth, benevolence), mirroring a moralistic bias. Where egoistic bias has been associated with power needs (e.g., the need for control and authority over others), moralistic bias has been linked to social desirability needs (e.g., the need for affiliation and social acceptance). Through a dynamic self-regulation process that interacts with one’s social environment (Morf & Rhodewalt, 2001), we predict different leadership behavior outcomes for agentic (i.e., destructive leadership behaviors) and communal (i.e., unauthentic leadership behaviors) narcissism based on regulation of these differing needs. The present paper delineates these predictions, and discusses theoretical and practical implications, as well as limitations and future research.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".