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Record W2955727868 · doi:10.1002/jocb.418

Creative Collaboration in Young Children’s Playful Group Drawing

2019· article· en· W2955727868 on OpenAlexaff
Tiina Kukkonen, Sandra Chang‐Kredl, Benjamin Bolden

Bibliographic record

VenueThe Journal of Creative Behavior · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsConcordia UniversityQueen's University
Fundersnot available
KeywordsPsychologyVariety (cybernetics)NegotiationMeaning (existential)CreativityDevelopmental psychologyMeaning-makingSocial psychologySociologyPsychotherapist

Abstract

fetched live from OpenAlex

ABSTRACT Collaborations that lead to creative outputs occur within different group contexts and with diverse populations, including young children. Two cases of collaborative drawing are presented in this article to consider how young children engage in creative collaboration by negotiating meaning with others through open‐ended group drawing. We conceptualize group drawing as a form of social play where children can advance personal creative abilities through interactions and shared understandings with others. The two cases derive from a study that examined young children's group play through drawing. A preschool class of 16 children (aged 4–5) was observed during free play over eight 1‐hour sessions. Children were free to come and go as they pleased from an art station consisting of large drawing surfaces and a variety of drawing materials. Findings from the two selected cases suggest the development of shared meaning supports creative collaboration in group drawing situations, as children use a variety of verbal and non‐verbal strategies to communicate their ideas. Implications are offered for early childhood educators and environments seeking to promote creative collaboration.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.014
GPT teacher head0.343
Teacher spread0.330 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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