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Record W2942565586 · doi:10.1111/lit.12190

Slow looking: “reading picturebooks takes time”

2019· article· en· W2942565586 on OpenAlexaff
Sylvia Pantaleo

Bibliographic record

VenueLiteracy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsReading (process)SituatedCentralityMeaning (existential)Focus (optics)Visual literacyDispositionPsychologyVisual artsPedagogyMathematics educationLinguisticsComputer scienceArtSocial psychology

Abstract

fetched live from OpenAlex

Abstract One of the main purposes of the classroom‐based research featured in this article was to develop 9‐year‐old students' visual meaning‐making skills and competences by focusing specifically on elements of visual art and design in picturebooks. The complexity of the picturebook format requires and rewards slow looking by readers/viewers. However, students need to receive instruction about and have opportunities for engaging in slow looking in order to develop the disposition to do so. The discussion of slow looking is situated in four frameworks that focus on the centrality of careful observation of images. Descriptions of some of the pedagogy delivered during the study that featured students engaging in slow looking and visual analysis are followed by examples of students' written work that reveals their close observation of and analytical thinking about elements of visual art and design in picturebook artwork.

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)0.013
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.008
GPT teacher head0.220
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 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

Citations37
Published2019
Admission routes1
Has abstractyes

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