Science Fiction and Political Imagination: ELLs Co-constructing Critical Social Justice Narratives in the ELT Classroom
Bibliographic record
Abstract
Abstract Engaging with social, political, and cultural topics can pose challenges in English language teaching classrooms. These sites represent linguistically diverse settings housing distinct ideologies. This article documents classroom interactions that began with exposure to a science fiction film and progressed to a discussion of contemporary sociopolitical concerns that resonated with ELLs. Drawing on this case study, the article explores how an instructor and her ELL students addressed sociopolitical topics in a science fiction–themed classroom. The author analyzes student–teacher and student–student interactions in the classroom and the instructor’s subsequent self-reflection. Findings suggest that, when adopting intellectually stimulating materials that foster ELLs’ agency in learning, teachers need scaffolding to meaningfully incorporate such tools into a critical pedagogical approach.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".