Transmediating race and senses through subtitling in translanguaging classroom
Bibliographic record
Abstract
This article reflects on a transdisciplinary curricular project in a Japanese language course that employs audio–visual subtitling to promote students’ critical literacies in languages, cultures, and media. Drawing on Nornes’s (2015) notion of “sensuous subtitling,” which embraces the incorporation of the “materiality of language” into translation, we present how students’ experimental subtitlings potentially make a transformative intervention into how the characters are sensed and felt through film viewing. As Japanese-speaking women teaching Japanese language and culture at an English-speaking university in Canada, we use our bodies as racialized and racializing sites to explore the affective potentials of interlingual subtitles and their pedagogical implications. We reflect on students’ subtitling of Korean Japanese film Where Is the Moon (1993, dir. Yoichi Sai) that embodies race and senses through various cinematic techniques. Students’ subtitlings reshaped the “intercorporeal” space in which the cinematic bodies of the characters touch our bodies (Sekimoto and Brown, 2020).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".