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Record W31039432 · doi:10.1016/j.bbi.2019.04.040

Communicating with Multimodalities and Multiliteracies

2013· article· en· W31039432 on OpenAlexaffvenue
Greg Paziuk

Bibliographic record

VenueTeaching Innovation Projects · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComicsAppealMeaning (existential)Context (archaeology)MultimodalitySociologyLiteracyPsychologyMedia studiesPedagogyLinguisticsPolitical scienceArtHistoryLiterature

Abstract

fetched live from OpenAlex

Comic books are currently enjoying a resurgence in both public and scholarly interest. However, most of that scholarly focus has focused on how to use comics to engage with reluctant readers; educators are still struggling to recognize the medium as a “complex form of multimodal literacy” (Jacobs 2007). In both their complexity and their appeal, comic books gesture towards multiliteracy, a term that recognizes “the context of our culturally and linguistically diverse and increasingly globalised societies; to account for the multifarious cultures that interrelate and the plurality of texts that circulate” (New London Group 2000). Multimodal forms, in general, have very recently become of particular interest to instructors seeking new tactics to engage students within increasingly diverse classrooms. Lisa Leopold (2012) highlights widening research suggesting that learning styles can vary, among other things, according to culture. The results demonstrate that in order to appeal to students of all backgrounds, educators need to consider auditory, visual, and kinesthetic models or ‘texts’ for understanding the concepts and theories they hope to convey. Multimodality, herein, becomes important as the term that is used to describe the different texts of meaning, or rather the convergence of these texts, where different forms of communication work both together and in contrast in order to convey meaning. Comic books provide a unique opportunity to explore this convergence, given that they are hybrid texts themselves. By asking participants to think critically about how different modes of communication can be incorporated into a comic book, this workshop encourages educators to reconsider how the modes in which they communicate to their students can appeal to all learners.\nA wealth of recent scholarship suggests that important revisions need to be made to the traditional concept of literacy. While text slang, gifs, and emoticons provide us with dramatic examples of the organic formation of systems of communication, educational reform is still centered on linguistic learning. In order to free ourselves from the conventional view of literacy as a simple matter of words, it is important to consider the multiple systems of meaning – or multiliteracies – that we all navigate as functional members of society. As Jacobs (2007) argues, when we analyze comics as examples of multimodal literacy, we practice ‘critical engagement’ that can then be translated to other multimodal texts that we encounter in our daily lives. In this workshop, participants will first be challenged to utilize these multiple literacies in a group reading of a comic book. In the concluding half, participants will use this experience to investigate how incorporating elements of multimodal design into their lessons can be beneficial in appealing to the widest possible audience.

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)0.000
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 score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.047
GPT teacher head0.264
Teacher spread0.217 · 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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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