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Record W3031729566 · doi:10.1080/01596306.2020.1769940

Through a lens of affect: multiliteracies, English learners, and resistance

2020· article· en· W3031729566 on OpenAlexaff
Julianne Burgess

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsAffect (linguistics)Resistance (ecology)Lens (geology)LinguisticsPsychologyAffect theorySociologyCommunicationSocial psychologyOpticsPhysicsPhilosophyBiologyEcology

Abstract

fetched live from OpenAlex

This autoethnographic study explores disruptive moments in a multiliteracies English-as-an-Additional Language (EAL) classroom with young adult students. Using a lens of affect theory, the study presents three strategic sketches (vignettes) to shine a light on unexpected intensities in the learning assemblage, initially assumed to be learner ambivalence, uneven investment and resistance, to multiliteracies pedagogy. Through autoethnographic inquiry, a writing of the self, this paper argues that affectively charged moments in literacy and language setting should be recognized as Deleuzian sense-events that are resistant to interpretation. The possibilities created by learning and teaching through sense-events and sensational pedagogies offer alternatives for doing multiliteracies and challenge the foundations of English language teaching, by proposing other ways of articulating meanings and experiences outside of language.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.018
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.376
Teacher spread0.265 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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