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Record W4308705876 · doi:10.1371/journal.pone.0276073

Sarcasm use in Turkish: The roles of personality, age, gender, and self-esteem

2022· article· en· W4308705876 on OpenAlexaff
Natalia Banasik‐Jemielniak, Piotr Kałowski, Büşra Akkaya, Aleksandra Siemieniuk, Yasemin Abayhan, Duygu Kandemirci-Bayız, Ewa Dryll, Katarzyna Branowska, Anna Olechowska, Melanie Glenwright, Maria Zajączkowska, Magdalena Rowicka, Penny M. Pexman

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
FundersNarodowe Centrum Nauki
KeywordsSarcasmTurkishNeuroticismPsychologyPersonalityAffect (linguistics)Big Five personality traitsSocioeconomic statusSelf-esteemShynessDevelopmental psychologySocial psychologyAnxietyMedicineIronyPopulationPsychiatry

Abstract

fetched live from OpenAlex

This study examined how self-reported sarcasm use is related to individual differences in non-Western adults. A sample of 329 Turkish speakers of high socioeconomic status completed an online survey including measures of self-reported sarcasm use, personality traits, positive and negative affect, self-presentation styles, self-esteem, as well as age and gender. Participants who reported being more likely to use sarcasm in social situations had scores indicating that they were less agreeable, less conscientious, and less emotional stable (i.e., more neurotic). Also, those who reported using sarcasm more often tended to be younger and had lower self-esteem. Self-reported sarcasm use was also positively related to both the self-promoting and the self-depreciating presentation styles. In addition to highlighting the complex relationship between individual differences and language production, these findings underscore the importance of expanding sarcasm research to include non-Western samples.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.283
Teacher spread0.191 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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