MétaCan
Menu
Back to cohort
Record W4375933533 · doi:10.4236/jss.2023.115003

Pop Culture Art as Educational Bridge: Connecting Generations through Games and Animation Movies in Classroom Environment

2023· article· en· W4375933533 on OpenAlexaff
Rafael Iwamoto Tosi

Bibliographic record

VenueOpen Journal of Social Sciences · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPopularitySemioticsSociologyAnimationAppealThe artsBridge (graph theory)Popular culturePerspective (graphical)MultimediaAestheticsProcess (computing)PerceptionMedia studiesVisual artsPsychologyEpistemologyComputer scienceArtSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This article seeks to shed light on the use of content from pop culture found in audiovisual media, especially those that have great appeal to younger generations. Starting the discussion from the critical principle that visual media today have enormous popularity among different age groups in our society, we seek to understand how the visual elements of pop culture were being engendered in a generalized way around the world. Based on these perceptions, we will understand how such elements can be used in the classroom as a pedagogical instrument with the use of theories of culture semiotics and edu-semiotics, where socio-cultural references will be verified in the perspective of creating bridges between students and teachers, carrying out a process known as the transgenerational process. Finally, the article contemplates the use of pop culture as an instrument of education and arts by presenting its essence, called heART, where artistic elements and social connection are essential to maintain a society in the constant flow of exchange of ideas and consequent life. In conclusion, it is clear that pop culture is still underutilized in educational contexts and that its achievements for the exchange of experiences with different generational peers are enormous.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.837

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 studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.355
Teacher spread0.268 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOpen Journal of Social SciencesSame topicLiteracy, Media, and EducationFrench-language works237,207