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Record W2714023110 · doi:10.21018/rjcpr.2017.1.231

Women Gossip and Men Brag: Perceived Gender Differences in the Use of Humor by Romanian Older Women

2017· article· en· W2714023110 on OpenAlexfundno aff
Ioana Schiau

Bibliographic record

VenueRomanian Journal of Communication and Public Relations · 2017
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyGossipRomanianInterpersonal communicationPerceptionSocial psychologySense of humorScale (ratio)Interpersonal relationshipDevelopmental psychology

Abstract

fetched live from OpenAlex

The present study investigates perceived gender differences in the producion and social use of humor in the interpersonal communication of Romanian older women, aged 60 and above. The study is a qualitative investigation, based on semi-structured interviews. The aim was to understand the perceptions and motivations that women have when using humor in social interactions, and to explore the functions that humor serves in their day-to-day communication. A previous quantitative investigation found statistically significant gender differences between Romanian older men and women on a sense of humor scale, and suggested that the use of humor in interpersonal communication had stronger social benefits for women (Schiau, 2016a). Drawing on these findings, and keeping in mind other studies that discuss the different use of humor by men and women, this study aims to investigate specific gender differences in the production of humor, as perceived by the participants.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.107
GPT teacher head0.323
Teacher spread0.217 · 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 designQualitative
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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