Reversed Subtitling: The Most Frequent Multiword Expressions in English-Subtitled Mandarin Dramas
Bibliographic record
Abstract
The drama fever has been riding high with consumer usage of over-the-top (OTT) streaming services on the rise and the prevalence of mobile devices with Internet connectivity. The researcher-teacher sometimes overhears her students chatting about the drama series they binge-watch. Given this binge-watching phenomenon on college campuses in Taiwan, where Mandarin is an official language and English as a foreign language (EFL) is a required course, the researcher-teacher is concerned about English lexical growth if Taiwanese students’ viewing habits shift from Mandarin to English subtitles. Deriving from Mandarin drama English subtitles, the researcher sought to create a list of the most frequent multiword expressions for students to learn because drama lines are often drawn from daily life and even reflect current events. A corpus of 8.46 million English-subtitled words from 50 Mandarin dramas across different genres was compiled, totaling 1,427 episodes. Based on frequency, dispersion and expert judgments, a total of 475 frequent multiword expressions were selected. Pedagogical implications and directions for future research are also discussed in the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".