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Record W4210531911 · doi:10.1002/job.2603

Loving or loathing? A power‐dependency explanation for narcissists' social acceptance in the workplace

2022· article· en· W4210531911 on OpenAlexaff
Erica Xu, Xu Huang, Sandra L. Robinson, Kan Ouyang

Bibliographic record

VenueJournal of Organizational Behavior · 2022
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarcissismOstracismPsychologySocial psychologyPopularityPerspective (graphical)Social comparison theoryPower (physics)

Abstract

fetched live from OpenAlex

Summary Research on the social consequences of narcissism points to an intriguing paradox: narcissists are socially aversive and destructive to healthy interpersonal relationships; yet, narcissists also have an ability to be socially magnetic and attractive. This raises the question we seek to answer in this paper: Are narcissists socially accepted by coworkers in the workplace, and if so, when? Drawing on the social‐constructionist perspective and power‐dependence theory, we propose that others' dependency on narcissists plays a critical role in determining narcissists' social acceptance in the workplace. Results from two time‐lagged independent studies suggest that narcissists with a high level of expertise status experience less ostracism than non‐narcissists, particularly in a group with high group goal interdependence; by contrast, narcissists who are perceived to lack expertise status experience greater ostracism than non‐narcissists, particularly in a group with low group goal interdependence. In Study 2, in addition to ostracism, we also examined social inclusion and popularity of narcissists, and we found that narcissists with high expertise status are more likely to be social included and to become popular, particularly in a group with a high level of group goal interdependence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.051
GPT teacher head0.373
Teacher spread0.322 · 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 teacher head, not a consensus.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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