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Record W3083946509 · doi:10.1080/03004279.2020.1818268

Elementary students meaning-making of the science comics series by first second

2020· article· en· W3083946509 on OpenAlexaff
Sylvia Pantaleo

Bibliographic record

VenueEducation 3-13 · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComicsMeaning (existential)SemioticsReading (process)Variety (cybernetics)Meaning-makingSelection (genetic algorithm)Mathematics educationVisual artsPsychologyArtComputer scienceLinguisticsLiteraturePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

During a classroom-based study, eight – to ten-year-old students had multiple opportunities to develop their knowledge and understanding about semiotic resources for meaning-making in picturebooks and graphic novels. Instruction during the study included a variety of activities that focussed on a selection of elements of visual art and design, and conventions of the medium of comics. A component of the study involved the Grades 4 and 5 students reading and discussing, and writing about two science graphic novels. Content analysis of the students’ responses to these multimodal ensembles revealed how the students identified, described, and interpreted various elements of visual art and design, and conventions of the medium of comics in the science comics as fulfilling multiple meaning-making purposes. The findings indicated that student learning about the what, why and how of design affected their aesthetic understanding of and critical thinking about the science graphic novels.

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)0.005
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.023
GPT teacher head0.263
Teacher spread0.240 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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