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Record W4253352025 · doi:10.32920/ryerson.14652549.v1

Preschool Children's Perceptions of Body Size Through Trait Associations and Drawings

2021· preprint· en· W4253352025 on OpenAlexaff
Caitlyn Leddy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentBrock University
Fundersnot available
KeywordsPsychologyTraitOverweightPerceptionAttributionDevelopmental psychologyTask (project management)Body shapeSocial psychologyMedicineBody mass index

Abstract

fetched live from OpenAlex

Limited studies have explored body size stigmatization in preschool children. The purpose of this study was to explore preschool children's perceptions of body size through the Adjective Attribution Task and the Drawing Task. Research was conduted with 23 preschool children from 36 months to 59 months old. Findings show that children associated more positive traits to the average size figure and more negative traits to the thin figure when presented with three figures of body sizes (thin, average, overweight). Children's responses revealed that they are aware of body sizes and in addition, they appear to stigmatize different body types. Future research in this area wil be important in order to help encourage the development of programs that can promote positive body image in young children. Furthermore, this study may encourage educators and practitioners working in community programs with children to use educational materials that reflect accurate depictions of body size.

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)0.006
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.366
Teacher spread0.335 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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