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Record W2941670199 · doi:10.1111/lit.12155

A forest‐based environment as a site of literacy and meaning making for kindergarten children

2018· article· en· W2941670199 on OpenAlexaffabout
Jodi Streelasky

Bibliographic record

VenueLiteracy · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsEducation and Early Childhood DevelopmentUniversity of Victoria
Fundersnot available
KeywordsExpansiveLiteracyContext (archaeology)Meaning (existential)PedagogyPsychologySpace (punctuation)Mathematics educationSociologyGeographyLinguistics

Abstract

fetched live from OpenAlex

Abstract This study analyses the valued school experiences of 15 five‐ and six‐year‐old Canadian children, through their creation of multimodal texts. Throughout the school year, the students spent a large portion of each school day in the expansive forest on the school grounds, and their texts revealed their significant interest in this natural outdoor environment. Specifically, the data revealed that the outdoor space provided a context where the children could engage with each other and the environment in meaningful, creative and collaborative ways. This research has the potential to contribute to our understanding of the capacity of young children to share their thoughts on their school experiences by drawing on a range of modes and to contribute to our understanding of the power of alternative learning spaces, such as forest environments, on children's literacy learning and development.

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)0.000
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 score0.350
Threshold uncertainty score0.696

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.015
GPT teacher head0.261
Teacher spread0.247 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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