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Record W4410504341 · doi:10.1027/2698-1866/a000100

Cross-Cultural Adaptation of the Short Dark Tetrad (SD4) in the Brazilian Context

2025· article· en· W4410504341 on OpenAlexaff
Carollina Souza Guilhermino, Bruno Bonfá-Araújo, Tiago Geraldo de Azevedo, Nelson Hauck Filho, Paulo Felipe Ribeiro Bandeira, Celso Francisco Tondin, Marco Antônio Silva Alvarenga

Bibliographic record

VenuePsychological Test Adaptation and Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsWestern University
Fundersnot available
KeywordsAdaptation (eye)TetradContext (archaeology)BusinessPsychologyGeographyBiologyBotany

Abstract

fetched live from OpenAlex

Abstract: The Short Dark Tetrad (SD4) is a personality scale developed to assess the Dark Tetrad traits: narcissism, Machiavellianism, psychopathy, and everyday sadism. No studies have explored its psychometric properties in Latin American samples, and few have examined gender biases. Thus, this study aimed to adapt the SD4 for Brazil, examine the four-factor model, and explore gender biases. The study included 753 adults aged 18–75 years ( M = 27.12; SD = 10.91). We found an adequate model fit for the four-factor solution, similar to the original measure. Furthermore, in Brazil, the SD4 presented measurement invariance for gender for the configural, metric, and scalar levels. And, when the means of men and women were compared, men showed higher levels for all four Dark Tetrad traits. The implications of these findings are discussed.

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)0.013
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.085
GPT teacher head0.384
Teacher spread0.299 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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