MétaCan
Menu
Back to cohort
Record W2522860907 · doi:10.5539/jel.v5n4p141

Comic Strips to Accompany Science Museum Exhibits

2016· article· en· W2522860907 on OpenAlexvenueno aff
Beom Sun Chung, Eun-mi Park, Sanghee Kim, Sook-Kyoung Cho, Min Suk Chung

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsComic stripComicsExhibitionSTRIPSVisual artsComprehensionCraftPsychologyArtComputer scienceLiteratureLinguisticsPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Science museums make the effort to create exhibits with amusing explanations. However, existing explanation signs with lengthy text are not appealing, and as such, visitors do not pay attention to them. In contrast, conspicuous comic strips composed of simple drawings and humors can attract science museum visitors. This study attempted to reveal whether comic strips contribute to science exhibitions. More than 20 comic strips were chosen that were associated with exhibits in a science museum. The individual episodes were printed out and placed beside the corresponding exhibits. A questionnaire was administered to museum visitors to evaluate the effects of the comic strips. Most visitors responded that the comic strips were helpful in understanding the exhibits and in familiarizing themselves with the science. Participants also described the comic strips’ deficiencies which will be considered for future revisions. Comic strips are likely to enhance interest in and comprehension of science exhibitions. Furthermore, these strips are expected to enrich science museums in various ways such as establishing their uniqueness.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0340.004

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 head0.027
GPT teacher head0.271
Teacher spread0.244 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Education and LearningSame topicMuseums and Cultural HeritageFrench-language works237,207