Reversed Subtitling and Extensive Reading: The Case of English-Subtitled Mandarin Dramas
Bibliographic record
Abstract
The binge-watching phenomenon on college campuses in Taiwan inspired this study. The researcher often overhears her students chatting about which Mandarin TV series they have been binge-watching recently. Given this drama fever, which may provide an impetus for sustained reading of on-screen text, the researcher is concerned with English vocabulary growth if the viewing habit shifts from Mandarin to English subtitles. A corpus of over 5.6 million English-subtitled words from 37 Mandarin dramas was compiled, totaling 1,238 episodes. The operational measures involved the ranked twenty-five 1000-word-family lists along the British National Corpus and the Corpus of Contemporary American English word-frequency scale. Results show that Mandarin drama English subtitles reached the 2000–3000 word-family levels at 95% text coverage and extended to the 4000–5000 levels at 98% coverage subject to genres. EFL Mandarin drama fans may encounter most words from each of the 1st to 6th 1000-word-family lists twelve times or more for potential learning by continually watching up to 24 English-subtitled Mandarin dramas. Moreover, twenty participants expressed their views on watching English-subtitled Mandarin dramas to a certain level of agreement. For extensive reading practitioners, the results may be a reference concerning what vocabulary level EFL learners may attain if they binge-watch English-subtitled Mandarin dramas in their leisure time.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".