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Record W3010414421 · doi:10.1111/jopy.12546

Being fun: An overlooked indicator of childhood social status

2020· article· en· W3010414421 on OpenAlexafffund
Brett Laursen, Robert L. Altman, William M. Bukowski, Wei Li

Bibliographic record

VenueJournal of Personality · 2020
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsConcordia University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsPopularityPsychologyNominationPerceptionPeer acceptanceDevelopmental psychologySocial psychologySocial statusTraitPeer group

Abstract

fetched live from OpenAlex

OBJECTIVE: The present study concerns an overlooked trait indicator of childhood peer status: Being fun. The study is designed to identify the degree to which being fun is uniquely associated with the peer status variables of likeability and popularity. METHOD: Two studies of children in grades 4 to 6 (ages 9 to 12) are reported. The first involved 306 girls and 305 boys attending school in northern Colombia. The second involved 363 girls and 299 boys attending school in southern Florida. Students completed similar peer nomination inventories, once in the first study and twice (8 weeks apart) in the second. RESULTS: In both studies, being fun was positively correlated with likeability and popularity. In the second study, being fun predicted subsequent changes in likeability and popularity, after controlling for factors known to be related to each. Initial likeability and popularity also predicted subsequent changes in perceptions of being fun. CONCLUSIONS: Anecdotal evidence suggests that children are intensely focused on having fun. The findings indicate that this focus extends beyond the immediate rewards that fun experiences provide; some portion of peer status is uniquely derived from the perception that one is fun to be around.

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 score0.622
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 head0.044
GPT teacher head0.351
Teacher spread0.307 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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