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Record W2965089683 · doi:10.1177/0261927x19865764

Global Second Language Proficiency Predicts Self-Perceptions of General Sarcasm Use Among Bilingual Adults

2019· article· en· W2965089683 on OpenAlexafffund
Mehrgol Tiv, Vincent Rouillard, Naomi Vingron, Sabrina Wiebe, Debra Titone

Bibliographic record

VenueJournal of Language and Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSarcasmPsychologyEmbarrassmentAffect (linguistics)CognitionPerceptionSocial psychologyCognitive psychologyLinguisticsCommunication

Abstract

fetched live from OpenAlex

Each culture has a distinct set of features that contribute to a unique communication style. For example, bilinguals often balance multiple social contexts and may undergo cognitive changes that consequently support different communication styles. The present work examines how individual differences in bilingual experience affect one form of communication style: sarcastic and indirect language. A diverse sample of largely bilingual adults (first language English) rated their likelihood of using sarcastic and indirect language across different daily settings. They also rated their second language experience. There were two key findings: Bilinguals use sarcasm for similar social functions as do monolinguals (general sarcasm, frustration diffusion, and embarrassment diffusion) and greater global second language proficiency linked to greater usage of general sarcasm in daily life. These results suggest that bilinguals may use sarcasm to achieve various communicative goals and bilingual experience may affect general cognitive capacities that support sarcasm use across real-world contexts.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.330
Teacher spread0.317 · 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

Citations24
Published2019
Admission routes2
Has abstractyes

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