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Record W4382603060 · doi:10.1002/jaal.1286

The social drama of digital multimodal composing: A case study with emergent bi/multilingual newcomer students

2023· article· en· W4382603060 on OpenAlex
Amir Michalovich

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Adolescent & Adult Literacy · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsThematic analysisPsychologyDramaPedagogyReflexivityLiteracySociologyPerspective (graphical)EthnographyQualitative researchMathematics educationComputer science

Abstract

fetched live from OpenAlex

Abstract This multi‐year, ethnographic, qualitative case study in English Language Learning classrooms contributes a unique analysis of nine adolescent newcomer students' investment in a digital multimodal composing (DMC) project as a social drama. Using reflexive thematic analysis, it explores the following possibilities afforded by in‐school, dramaturgically structured DMC processes for the students' investment in classroom learning: (1) changing the definition of the situation, (2) supporting students' impression management to gain social and cultural capital, and (3) creating bonds of reciprocal dependence and familiarity. The study helps language and literacy researchers, educators, and teacher educators better understand emergent bi/multilingual newcomer students' investment in DMC processes through the sociological perspective of dramaturgy, suggesting how DMC might deepen learning while valuing the assets of culturally, linguistically, and racially diverse newcomer students.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.877

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.032
GPT teacher head0.342
Teacher spread0.310 · 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