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Record W3088991212 · doi:10.1002/tesj.551

Using social justice graphic novels in the ELL classroom

2020· article· en· W3088991212 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

VenueTESOL Journal · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsCentennial College
Fundersnot available
KeywordsJournaling file systemPedagogyPsychologyVisual artsMathematics educationSociologyComputer scienceArt

Abstract

fetched live from OpenAlex

Abstract Graphic novels are a form of authentic text that have started to gain widespread acceptance in the English language arts field and have been shown to increase students’ motivation to read and engage deeply with texts. By integrating text with pictures, graphic novels have the advantage of requiring a lighter cognitive load than traditional full‐text novels and can be more visually and emotionally impactful. This article discusses the need for graphic novels in the English language learner (ELL) classroom and their benefits as authentic, multimodal texts that lower the obstacles to engaging with challenging social justice issues. The article provides a sample unit plan that takes an in‐depth look at the graphic novel trilogy March by John Lewis, which provides a firsthand account of the U.S. civil rights movement. The unit plan and supplementary resources can be adapted to facilitate discussion in ELL classrooms worldwide regarding a variety of equity‐oriented issues. The article explores how graphic novel texts can engage students in deeper thinking about difficult issues through readings, discussions, journaling, and completing research projects on social justice themes worldwide.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.606

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.177
GPT teacher head0.298
Teacher spread0.121 · 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