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Record W2801458778 · doi:10.18357/jcs.v42i4.18103

Children, Outdoor Play, and Loose Parts

2018· article· en· W2801458778 on OpenAlexaffvenue
Caileigh Flannigan, Beverlie Dietze

Bibliographic record

VenueJournal of Childhood Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsOkanagan CollegeSt. Francis Xavier University
Fundersnot available
KeywordsPsychologyDevelopmental psychologyNonverbal communicationSocial psychology

Abstract

fetched live from OpenAlex

In an effort to understand how loose parts in early learning environments benefit children’s play and development, through observations of preschool children in a rural outdoor natural environment, this study examined behaviours that children exhibited as they used loose parts. Children exhibited a range of positive social behaviours, complex verbal and nonverbal behaviours, and various types of risk taking in their play. The findings suggest that children do not explicitly exhibit stereotypical gender or age-exclusion behaviours while using the loose parts. The results would suggest that increasing children’s opportunities to use loose parts in an early years outdoor environment will support their various aspects of development in positive ways.

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)0.002
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.358
Teacher spread0.318 · 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

Citations79
Published2018
Admission routes2
Has abstractyes

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