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Record W2626548254 · doi:10.21913/jps.v4i1.1419

Using engaged philosophical inquiry to deepen young children’s understanding of environmental sustainability: Being, becoming and belonging

2017· article· en· W2626548254 on OpenAlexaff
Margaret MacDonald, Warren Bowen, Cher Hill

Bibliographic record

VenueJournal of Philosophy in Schools · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsRigourSituatedResidenceSustainabilityStewardship (theology)PsychologySociologyPedagogyDevelopmental psychologyEcologyEpistemologyPolitical scienceDemography

Abstract

fetched live from OpenAlex

This research paper shares findings related to our use of Engaged Philosophical Inquiry (EPI) with a group of young children (aged 3-4) as a pedagogical method taken up to extend young children’s thinking about human use of forest parkland and to determine the children’s ontological positions related to environmental sustainability. The study was conducted in a forested area adjoining a ‘living building’ childcare centre. Here researchers, along with a core group of 9-13 children, their teachers, and a Philosopher-in-Residence (Warren Bowen) visited the forest environment on a fortnightly basis over a four-month period from January to May 2016 to explore the forested area, play games and discuss issues related to forest use and human habitation. Video records of the EPI sessions were transcribed and analysed to determine the children’s propositions and related ontological stance(s) across sessions. Findings from this study include: (1) evidence that young children’s views on stewardship are situated within socio-material manifestations of belonging, ownership, and entitlement within the forest; and (2) that absurdities, along with other more traditional EPI and P4C strategies, can be used successfully to playfully challenge young children’s thinking about the rigour of their propositions and to provoke deeper thoughts related to belonging and care.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.382
Teacher spread0.270 · 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 teacher head, not a consensus.

Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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