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Record W2928235982 · doi:10.1080/21594937.2019.1582844

Avoiding a dystopian future for children's play

2019· article· en· W2928235982 on OpenAlexaff
David J. Ball, Mariana Brussoni, Tim Gill, Harry Harbottle, Bernard Spiegal

Bibliographic record

VenueInternational Journal of Play · 2019
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScope (computer science)DystopiaRisk analysis (engineering)Value (mathematics)PsychologyBusinessMedicinePublic relationsForensic engineeringEngineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Children’s play is increasingly controlled, costly and standardised. Risk aversion has resulted in attempts to eliminate all danger despite the limited health burden of play-related injuries and missing cost–benefit evidence. The current role and implementation of playground safety standards is a key inhibitor of stimulating play provision. Playground safety standards assume that play is about engineered structures. Standard creating bodies and playground inspectors tend to be missing key voices and knowledge. The proper domain of playground standards is engineering-related issues, such as structural integrity. However, they make judgments about children’s play behaviours, which are shaped by the children using the space and local circumstances: they are not standardised and should lay outside the scope of standards. We recommend that the value-based judgments currently included in standards and inspections should rest with play providers using Risk Benefit Assessment frameworks. We provide recommendations for play providers, standard setters, inspectors and public health.

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)0.025
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.029
Scholarly communication0.0180.017
Open science0.0030.016
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0130.002

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 head0.010
GPT teacher head0.321
Teacher spread0.311 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations38
Published2019
Admission routes1
Has abstractyes

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