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Record W4400412540 · doi:10.1080/13504622.2024.2374326

‘What is natural in natural playgrounds?’: nature, sustainability and environmental education in Calgary’s natural playgrounds

2024· article· en· W4400412540 on OpenAlexafffundabout
Miho Trudeau

Bibliographic record

VenueEnvironmental Education Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Calgary
FundersFederation for the Humanities and Social Sciences
KeywordsNatural (archaeology)Environmental educationSustainabilityOutdoor educationNatural resourceEnvironmental ethicsEnvironmental planningEnvironmental protectionSociologyEnvironmental scienceGeographyPedagogyEcologyArchaeology

Abstract

fetched live from OpenAlex

Although natural playgrounds have originated from an intention to provide greater access to nature for children, these play spaces offer a designed and regulated space that diverges from other natural contexts. This study investigated the perceived and sociomaterially constructed role of nature in natural playgrounds by examining what people (i.e. playground designers and users) perceive and experience as natural within five natural playground sites in Calgary, Alberta. Drawing from interviews with playground developers, caregivers, and children that use the natural playground sites, this study describes what features are perceived as natural within the playgrounds, what shapes these forms of nature, and how these forms of nature may contribute to sustainable learning environments. This study found several opportunities and challenges surrounding sustainability, including the use of sustainable design within sites, the tension of sharing sites with non-human users, and the potential for further environmental education embedded within site design.

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)0.001
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 score0.385
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.010
GPT teacher head0.348
Teacher spread0.338 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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