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Record W1991261496 · doi:10.1111/1467-8624.00324

The Influence of Group Size on Children's Competitive Behavior

2001· article· en· W1991261496 on OpenAlexafffund
Joyce F. Benenson, Angela Waite, Rosanne Roy, Anna Simpson

Bibliographic record

VenueChild Development · 2001
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsMontreal Children's HospitalCanadian Respiratory Research NetworkMcGill University
FundersMcGill University
KeywordsPsychologyDevelopmental psychologyGroup dynamicSocial relationTest (biology)Social psychologyEcology

Abstract

fetched live from OpenAlex

The present research was designed to test the hypothesis that children would compete more in tetrads than in dyads. Twenty-two pairs of male and 14 pairs of female target children (N = 72) played a competitive game in both tetrads and dyads. Consistent with the hypothesis, male target children competed more in tetrads than in dyads. This hypothesis was not supported for females, however. Analyses of the dynamics of tetrads and dyads further demonstrated that based on a global measure of smiling, the emotional atmosphere was less positive in tetrads than in dyads. The causes and consequences of interaction in different sized social groups are discussed.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.262
Teacher spread0.252 · 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

Citations68
Published2001
Admission routes2
Has abstractyes

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