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Record W2801635052 · doi:10.1002/trtr.1701

Multimodal Becoming: Literacy in and Beyond the Classroom

2018· article· en· W2801635052 on OpenAlexfundno aff
Kimberly Lenters

Bibliographic record

VenueThe Reading Teacher · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMultimodalityFraming (construction)ConversationLiteracyPsychologyPedagogyCritical literacyCritical thinkingMathematics educationLinguisticsCommunication

Abstract

fetched live from OpenAlex

Abstract The author explores the possibilities that posthumanist thinking offers for amplifying our understanding of multimodality in children's literacies in school and beyond. Drawing on data from a five‐month case study on the multimodal literacy practices of six fifth‐grade students across home, community, and school settings, the author focuses on one 10‐year‐old student. The author uses the student's engagement with graphic novels as a starting place for considering what students’ entanglements with multimodal literacies beyond the classroom can teach us about multimodality in classrooms. The author first discusses multimodality as it is typically framed and then puts this framing into conversation with posthumanist perspectives on literacy learning to open up considerations of what counts as multimodality. Finally, the author discusses ways that thinking with posthumanist concepts such as affect, embodiment, relationship, movement, and place can enhance both multimodal literacy instruction and students’ engagement with literacy.

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)0.002
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.015
Scholarly communication0.0070.006
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.033
GPT teacher head0.284
Teacher spread0.252 · 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

Citations37
Published2018
Admission routes1
Has abstractyes

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