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Record W3047817537 · doi:10.3390/educsci10080203

“Making” Waves: How Young Learners Connect to Their Natural World through Third Space

2020· article· en· W3047817537 on OpenAlexafffund
Anne Burke, Abigail Crocker

Bibliographic record

VenueEducation Sciences · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaMemorial University of Newfoundland
KeywordsSpace (punctuation)Context (archaeology)Natural (archaeology)Sociocultural evolutionSociologyValue (mathematics)PsychologyGeographyComputer science

Abstract

fetched live from OpenAlex

In a world that grows increasingly aware of ecological problems such as global warming, rising sea levels, and pollution, we need to reconsider how we connect ourselves to the natural world around us. In this paper, we view makerspaces as ideal locations to shape children’s emotional, sociocultural, and educational consciousnesses about the environment and our multi-layered roles undertaken to live in, and conserve, it. We apply third space, makerspace, and relational value theories in the analysis of a research project conducted with children at an early childhood centre. This project invited children to discuss ocean conservation prompted by the picturebook Flotsam (2006) and create three-dimensional exhibits that express how they visualize ocean conservation. Our research shows that children develop strong emotional connections to tangible representations of conservation when they are given the time to invest in making them, and that these emotional connections are driving forces for relational values that create conservation-oriented mindsets. It also shows how important context is for shaping the ways children learn, and that providing opportunities to examine conservation through makerspaces as a third space encourages children to create empathetic and personal relationships with the natural world.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.140
GPT teacher head0.407
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2020
Admission routes2
Has abstractyes

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