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Record W2993175982 · doi:10.5617/nordina.6169

The Blue Car in the Forest: Exploring Children’s Experiences of Sustainability in a Canadian Forest

2019· article· en· W2993175982 on OpenAlexaffabout
Debra Harwood

Bibliographic record

VenueNordic Studies in Science Education · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsBrock University
Fundersnot available
KeywordsSustainabilityEthnographyParticipant observationPsychologyQualitative researchScale (ratio)PedagogySociologyGeographySocial scienceEcologyAnthropologyCartography

Abstract

fetched live from OpenAlex

An abandoned blue car from 1958 is a central figure of the qualitative exploration of sustainability pedagogies within a Canadian nature school. The mystery of the car and its entanglement within a densely-forested area where the preschool children play and learn is provocative. As part of a larger ethnographic case study of the nature school, eight young children (3-5-year-olds) and their two nature teachers’ critical engagement with the car is examined over the course of a year. The research approach for the data collection and analysis included photos, videos, participant-observations, educator journals, and children’s oral and written expressions of their ideas related to the project and sustainability. This small scale study offers a glimpse into the possibilities that emerge when we include children’s thinking, decisions, and actions within the more-than-human world to foster sustainability.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.053
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.011
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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