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Record W4404464109 · doi:10.1027/1015-5759/a000864

The Short Dark Tetrad (SD4)

2024· article· en· W4404464109 on OpenAlexaffabout
Bojana M. Dinić, Erin E. Buckels, Nataša Kovačević

Bibliographic record

VenueEuropean Journal of Psychological Assessment · 2024
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsTetradPsychologyBotanyBiology

Abstract

fetched live from OpenAlex

Abstract: This study aimed to test the construct and criterion validity of the Serbian adaptation of the Short Dark Tetrad (SD4). In addition to testing measurement invariance between the Serbian ( N = 488) and Canadian samples ( N = 739), the construct validity of the SD4 was also assessed through correlations with more extensive measures of the Dark Tetrad, and criterion validity was evaluated through correlations with variables related to mental health. The results indicated good model fit indices for the SD4 in both samples and partial scalar invariance across samples. Validity correlations with extensive measures confirmed the construct validity of all SD4 scales, with caution noted for the psychopathy scale, which shares similar content with sadism. Profile similarity, based on construct and criterion correlations, revealed substantial dissimilarity between narcissism and the other scales but high similarity among the Machiavellianism, psychopathy, and sadism scales. Regardless of the similarity between the scales, they showed distinctive correlations with emotional distress and positive mental health aspects.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.084
GPT teacher head0.439
Teacher spread0.355 · 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 designBench or experimental
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

Citations7
Published2024
Admission routes2
Has abstractyes

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