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The Dark Triad of Personality and its Relationship to Leadership, Management, Team Work and Influencing Behaviours, and 360 Degree Assessments of Satisfaction

2017· article· en· W2739384113 on OpenAlexvenueno aff
Tony Manning

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2017
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsDark triadMachiavellianismPsychopathyNarcissismPsychologySocial psychologyPersonality psychologyPersonalityDegree (music)Applied psychology

Abstract

fetched live from OpenAlex

The ‘Dark Triad’ refers to three socially-aversive personalities, namely, Machiavellianism, narcissism and psychopathy. Previous research shows both how such behaviours can be both counter-productive and advantageous. However, there is little on how the Dark Triad is linked to specific workplace behaviours. This article fills this gap by looking at the relationship between measures of the Dark Triad and self-assessments of leadership, management, team working and influencing behaviours, as well as with 360 degree assessments of such behaviours. It identifies particular behaviours that are used by each of the three personality types, along with 360 degree assessments of such behaviours. Given that the Dark Triad exists and that it has implications for the workplace behaviours, organisations have to deal with such behaviours. The article ends by considering the practical implications of the research findings, including issues around selection and placement, team composition and training and development. Finally, it suggests areas for further research.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.245
GPT teacher head0.416
Teacher spread0.171 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicPersonality Traits and PsychologyFrench-language works237,207