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Record W4401705146 · doi:10.1080/14729679.2024.2393133

Monkey bars and medical masks: a photo-voice study of children’s playground safety in British Columbia, Canada

2024· article· en· W4401705146 on OpenAlexafffundabout
Michelle E. E. Bauer, Ildiko Gabriella Kovács, Ian Pike

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChild safetyPsychologyEngineering

Abstract

fetched live from OpenAlex

Children’s perspectives on their outdoor play safety can provide unique insight into what activities and environments are injurious for them. In this study, we conducted photo-elicitation interviews with 13 children (7 girls, 6 boys) from low- to mid-income communities who were between the ages of 9- to 13-years-old. The goal of the study was to examine their perspectives of outdoor play safety around urban playgrounds over a two-week period while they participated in day camp activities. Our thematic content analysis resulted in three themes: (1) fear of falling; (2) illness prevention; and (3) need for excitement. Our findings suggest children’s perception of safe play may be shaped by exposure to sanitary practices throughout the COVID-19 pandemic. Further, findings demonstrate children can experience tensions in participating in desirable play that is exciting, novel, and challenging, while fearing they could fall from higher structures that afford them with these opportunities.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0130.004
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.067
GPT teacher head0.479
Teacher spread0.411 · 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 designQualitative
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
Published2024
Admission routes3
Has abstractyes

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