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Record W4253213584 · doi:10.32920/ryerson.14664972

"You told me I couldn't play there": an autoethnographic exploration of children's outdoor play

2021· preprint· en· W4253213584 on OpenAlexaff
Nicola Maguire

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood Development
FundersComic Relief
KeywordsAutoethnographyNarrativePerceptionEarly childhoodValue (mathematics)Outdoor educationPsychologyFocus (optics)SociologyDevelopmental psychologyAestheticsPedagogySocial psychologyGender studiesArtComputer scienceLiterature

Abstract

fetched live from OpenAlex

Playing outdoors is an essential component of childhood yet that play is often bound by adult perceptions of safety, risk, and children’s capabilities. Research reflects the positive value that playing freely outdoors has in terms of children’s overall development. However, literature also highlights the impact of a societal focus on safety, which can limit young people’s access to the outdoors and the types of play that they seem to enjoy. The tension that can exist between pedagogical practices and trusting children to be safe plays out within the structure and format of this paper as the motifs of bounding and resistance that can exist in both research and play are unearthed. Drawing on autoethnographic and narrative approaches I explore, share, and reflect upon outdoor play experiences from my own childhood as a means to gain a deeper understanding of how children were and are positioned in society and communities.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.018
Scholarly communication0.0070.005
Open science0.0020.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.363
Teacher spread0.316 · 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
Published2021
Admission routes1
Has abstractyes

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