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Record W4414770375 · doi:10.1016/j.jrp.2025.104671

Narcissism and the appraisal of status-related social cues

2025· article· en· W4414770375 on OpenAlexafffund
Breanna E. Atkinson, Erin A. Heerey

Bibliographic record

VenueJournal of Research in Personality · 2025
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWestern University
FundersWestern University
KeywordsNarcissismTraitBig Five personality traitsPersonalityAggressionDominance (genetics)PreferenceSocial perceptionAttribution bias

Abstract

fetched live from OpenAlex

The personality trait, narcissism, is characterized by grandiosity, entitlement, and a heightened drive for social status. Narcissism may therefore influence how people appraise status-relevant social cues. Study 1 examined affective appraisals of status-relevant cues (e.g., “boss”, “assistant”) using an implicit appraisal task. Contrary to expectations, narcissism failed to moderate task performance for stimuli associated with existing social ranks. In Study 2, participants completed a choice-preference task examining positive and negative trait adjectives associated with status pursuit (“ambitious”, “antagonistic”). Results showed a robust relationship between traits associated with negative methods of status pursuit and self-reported narcissism, suggesting that narcissistic individuals may find the use of anti-social tactics (e.g., antagonism, dominance, rivalry) both less off-putting and more desirable in themselves and others.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.113
GPT teacher head0.521
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractno

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