Effects of Bimodal Subtitling of English Movies on Content Comprehension and Vocabulary Recognition
Bibliographic record
Abstract
This thesis is an attempt to study the impact of bimodal subtitling on content comprehension of English movies and vocabulary recognition. Forty four senior undergraduate students studying at Shiraz Islamic Azad University were selected from two intact classes of Tapes and Films Translation course. Two BBC documentary movies (Dangerous knowledge and Where’s my robot?), one with English subtitles and the other without subtitles were selected based on the content and level of difficulty of the language. First, both classes watched the same movies, but class 1 first watched ‘Dangerous knowledge’ with English subtitling and then ‘Where’s my robot?’ without subtitling. To counteract the order effect class 2 first watched ‘where’s my robot?’ and then ‘Dangerous knowledge’. After viewing the movies, the participants answered the relevant multiple choice vocabulary and content comprehension questions. The data gathered were subjected to the statistical procedure of paired samples t-test. The results clearly indicated that bimodal subtitling had a positive impact on content comprehension of English movies. It can be said that the participants comprehend the subtitled movie better than the one without subtitle. However, for some reasons bimodal subtitling did not have an effect on participants’ vocabulary recognition.
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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 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".