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Record W3156465903 · doi:10.1007/978-3-030-49679-1_7

Language in Digital Motion: From ABCs to Intermediality and Why This Matters for Language Learning

2020· book-chapter· en· W3156465903 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsYork University
Fundersnot available
KeywordsMultimodalityLinguisticsSemioticsComputer sciencePerspective (graphical)Language acquisitionEmojiTransition (genetics)Cognitive sciencePsychologyArtificial intelligenceSocial mediaWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Linguistic communication has moved beyond simple alphabetic encoding to multimedia design, challenging the fit of structural theories of language to digital communication. This transition is barely evident in mobile language learning contexts where top-selling apps present language as a linguistic structure to be drilled, ironically bypassing the complex communicative potential of smart devices. This chapter overviews changing language norms from language as structure to language within multimodality and comparatively discusses multimodality from a social semiotics paradigm nested in linguistic theory and from Elleström’s intermediality paradigm. To illustrate how one could conceptualize multimodality from a perspective decentred from linguistics and leveraged to explain language use in multimedia contexts, the author examines two novel features of digital communication: emoji and conversational digital agents .

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.958
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.247
Teacher spread0.230 · 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

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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Same topicDigital Communication and LanguageFrench-language works237,207