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Record W1977977344 · doi:10.1155/2013/390256

A New Tool to Explore Children’s Social Competencies: The Preschool Competition Questionnaire

2013· article· en· W1977977344 on OpenAlexaff
Daniel Paquette, Marie-Noëlle Gagnon, Luc Bouchard, Marc Bigras, Barry H. Schneider

Bibliographic record

VenueChild Development Research · 2013
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of OttawaUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsDominance (genetics)PsychologyAggressionHierarchyCompetition (biology)Developmental psychologyDominance hierarchyExploratory factor analysisExploratory analysisTask (project management)Exploratory researchSocial psychologyPsychometricsPolitical scienceSociologyEcologyData scienceManagementComputer scienceSocial scienceChemistryEconomics

Abstract

fetched live from OpenAlex

This paper presents the validation of Preschool Competition Questionnaire (PCQ). The PCQ was completed by the childcare teachers of 780 French-speaking children between the ages of 36 and 71 months. The results of exploratory factor analysis suggest three dimensions involving neither physical nor relational aggression: other-referenced competition, task-oriented competition, and maintenance of dominance hierarchy. The three dimensions are positively correlated with dominance ratings and are linked to social adjustment. Girls are just as competitive as boys in the dimensions of other-referenced competition and dominance hierarchy maintenance. Task-oriented competition is relatively more important in older children and girls. Classification analysis reveals that the children who obtain the highest dominance ratings are the ones who employ a variety of competition strategies.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.057
GPT teacher head0.347
Teacher spread0.290 · 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 designBench or experimental
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

Citations17
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueChild Development ResearchSame topicBullying, Victimization, and AggressionFrench-language works237,207