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Record W2322966995 · doi:10.1177/1468798415569816

Primary students’ understanding and appreciation of the artwork in picturebooks

2015· article· en· W2322966995 on OpenAlexaff
Sylvia Pantaleo

Bibliographic record

VenueJournal of Early Childhood Literacy · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMultimodalitySemioticsSociocultural evolutionPicture booksReading (process)LiteracyReading comprehensionPsychologySelection (genetic algorithm)SituatedPedagogyVisual literacyVisual artsInterpretation (philosophy)LinguisticsSociologyComputer scienceArt

Abstract

fetched live from OpenAlex

One of the purposes of the classroom-based research featured in this article was to explore how the ongoing development of young children’s understanding of elements of visual art and design would affect their comprehension, interpretation and analysis of the artwork in a selection of picturebooks. Social semiotics, multimodality, sociocultural theory and transactional theory framed the study, as well as the analysis and discussion of the data featured in this article. Further, the reading of and writing about the picturebooks were situated in the four roles/practices required for reading multimodal and visual texts. During a nine-week period, 22 seven- and eight-year-old students participated in several activities that focused on learning about specific elements of visual art and design. The students read, talked about and responded in writing to a selection of picturebooks. This article features an analysis of the students’ responses to two picturebooks and discusses what the students’ text-based writing reveals about their understanding and appreciation of the artwork in these multimodal texts. The article concludes with a discussion of the importance of teaching elements of visual art and design in order to develop students’ visual literacy skills and repertoires of capability with respect to reading multimodal texts.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.250
Teacher spread0.212 · 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 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

Citations47
Published2015
Admission routes1
Has abstractyes

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