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 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".