Using social justice graphic novels in the ELL classroom
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".