Mapping reflexivity in situ: A multimodal exploration of negotiated textbook discourses in Korean university EFL classrooms
Bibliographic record
Abstract
The present study identifies and maps the reflexive praxis of two experienced English as a foreign language (EFL) instructors as they reconstruct and negotiate textbook material in situ. An abundance of critical studies underscoring social injustices in the contents of globally published EFL textbooks do not sufficiently address the negotiation of their multimodal discourses during class time. Although reflexive teaching practice in language learning classrooms has a robust pool of research, limited scholarly attention has been given to the active negotiation of a textbook’s multimodal discourse in Korean university classrooms. The present study asks: (1) How do two instructors at different Korean universities negotiate the contents of an EFL textbook with their students during class? (2) How do the students react to the multimodal discourse negotiated in their textbooks? (3) What pedagogical implications do the findings lend to EFL textbook instruction in Korean university contexts? Using Norris’ (2004) framework for video transcription of multimodal interaction in two Korean university English communication classes, the findings reveal that reflexive negotiation between students and instructors is a kind of rhetorical accomplishment that lessens the potential for cultural marginalization in the multimodal discourse of EFL textbooks. Implications suggest that textbook reflexivity in situ raises the value of student EFL learning investments.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".