Subtitling as a Pedagogical Tool for Language Teaching in Journalism Courses
Bibliographic record
Abstract
This study was designed to present and discuss some results produced by a research involving the use of English subtitles of some news videos from the webiste Reuters.com (http://www.reuters.com) with pedagogical reasons in a Brazilian context (Academic English for Journalism). We have developed the research during two semesters at UNESP (Universidade Estadual Paulista Júlio de Mesquita Filho). The professor in charge of the study has chosen the students of Journalism as the audience to whom the videos were presented. The assumptions of many theorists and experts in Audiovisual Translation were adopted as our Theoretical Sources. The first step of the study was the assessment of the syllabus of each course. This was very helpful as a guidance in order to choose the most relevant and interesting videos for students. After the evaluation of academic and professional interests, we chose some videos to insert appropriate subtitles, according to some strategies suggested by Panayota Georgakopoulou and Henrik Gottlieb. Finally we presented the videos during the English classes. At the first time, they were presented without subtitles just to notice the comprehension level of the students. After that, the videos were presented with English subtitles. As we first assumed, the students haven’t had the whole comprehension of specific details during the first presentation, they have just used their previous knowledge and the visual aids to help them in a superficial understanding of the news. As the subtitles appear, the process of communication was finally accomplished.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".