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Record W4297538883 · doi:10.3390/educsci12100653

Rewilding Play: Design Build Interventions

2022· article· en· W4297538883 on OpenAlex
Susan Herrington, Ivana Lexa-French, Mariana Brussoni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEducation Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsLearning PartnershipBC Children's HospitalUniversity of British Columbia
FundersLawson Foundation
KeywordsPsychological interventionAffordanceAgency (philosophy)Medical educationIntervention (counseling)PsychologyIndigenousApplied psychologyPedagogyPublic relationsSociologyPolitical scienceMedicineEcology

Abstract

fetched live from OpenAlex

Research on physical interventions installed in outdoor environments and their impacts on children’s play and development is a growing area of study. This paper focuses on the design and installation of outdoor interventions at early childhood education centres in Vancouver, Canada and the impact that theses interventions had on play affordances. With the aim of intervening with inexpensive natural materials and loose parts, graduate students designed, built, and installed interventions and using the Seven Cs evaluation form they scored the play spaces pre- and post-installation. Design methods included the Seven Cs design guidelines and the Two-Eyed Seeing model. Students also sought the insights of Early Childhood Educators, maintenance staff, licensing officers, the British Columbia Cancer Agency, and an Indigenous herbalist/educator. They also examined and addressed solar modifications to create dappled light. To understand the impacts of the student interventions researchers compared the pre- and post-intervention Seven Cs scores, which increased by 20 to 30 points. Researchers seeking to replicate this type of project in their own institutions should carefully consider the impact of climate change on construction timing and material selection, and sensitivity to the diversity of socio-cultural values embedded in the community and within design decisions and the interventions themselves.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.105
GPT teacher head0.372
Teacher spread0.267 · 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