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Record W3015522517 · doi:10.1002/icd.2180

The impact of gossip valence on children's attitudes towards gossipers

2020· article· en· W3015522517 on OpenAlexaff
Adrianna Ruggiero, Emily Parolin, Lili Ma

Bibliographic record

VenueInfant and Child Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGossipPsychologyPopularityAscriptionSocial psychologyValence (chemistry)Developmental psychologySociometryPerceptionContext (archaeology)Linguistics

Abstract

fetched live from OpenAlex

Abstract This research explored children's attitudes towards gossipers in relation to gossip valence. Four‐ to 8‐year‐old children ( N = 214) read three storybooks containing positive, neutral, or negative gossip statements. Following each book, children were interviewed on whom they viewed as nicer and more honest (ascription of desirable traits), whom they preferred to interact with (social preference), and whom they thought had more friends (perceived popularity), by choosing between a gossiper and a non‐gossiping character. The results indicated that overall, children held more favourable attitudes towards gossipers who made positive than negative or neutral statements about a target. This effect of gossip valence was more pronounced for 6‐ to 8‐year‐olds than for the younger children on the ascription of desirable traits. The findings will add to our understanding of how gossip serves as a source of social influence on children, and may have real‐world implications with regard to children's peer interactions in school context. Highlights This research explored children's attitudes toward gossipers in relation to gossip valence. Four‐ to 8‐year‐olds held more favorable attitudes toward gossipers who made positive than negative or neutral statements about a target. The findings may have real‐world implications with regard to children's social perception and peer interactions in school context.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.661

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.322
Teacher spread0.287 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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