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Record W1976599711 · doi:10.5539/jel.v1n1p49

Classroom Aesthetics in Early Childhood Education

2012· article· en· W1976599711 on OpenAlexaffvenue
Linda Apps, Margaret MacDonald

Bibliographic record

VenueJournal of Education and Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransformative learningEmbodied cognitionCurriculumSpace (punctuation)AestheticsPedagogyElement (criminal law)Early childhood educationPsychologyThe artsPoint (geometry)Relation (database)Mathematics educationLanguage artsSociologyVisual artsEpistemologyArtComputer scienceMathematics

Abstract

fetched live from OpenAlex

There has been a growing awareness of the contribution of aesthetics to the pedagogical experience of young children. Aesthetics along with classroom design and curriculum generates implicit and explicit messages that impact and inform the pedagogical process. While discrete elements of classroom design can be defined and taught to early childhood educators, the aesthetic element is less accessible as a point of entry, particularly in relation to how the classroom and curriculum are activated, engaged with and embodied. Given the transformative nature of classroom spaces, it may be better to describe classrooms as holding an aesthetic that is determined and defined by those who occupy and participate in the space at any particular time. The following article will discuss ways that we might begin to articulate and apply an aesthetic lens to early learning classrooms using an arts-informed framework to critique play-based classroom space purposed for children’s exploration and inquiry.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.022
Scholarly communication0.0090.003
Open science0.0010.006
Research integrity0.0010.003
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 head0.014
GPT teacher head0.328
Teacher spread0.314 · 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 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

Citations16
Published2012
Admission routes2
Has abstractyes

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