Teaching Culture and Identifying Language Interference Errors through Films
Bibliographic record
Abstract
This study reflects intermediate level learners’ opinion about employing films in the EFL classroom for teaching culture and avoiding negative language transfer. A total of 63 participants, aged 21-23, took part in the experiment in the Faculty of Philology at Suleyman Demirel University in Almaty, Kazakhstan. During the experiment the subjects were demonstrated six extracts with culture laden scenes in a time range of 1-3 minutes. Participants had to detect peculiarities of American and Japanese cultures and compare them with Kazakh traditions. In the second part of the experiment, the subjects had to dub the film clips from Russian into English and compare their work with the original sequence. The study showed that the participants enjoyed both activities, and were ready to do them on a regular basis. Students also claim that Japanese and Kazakh cultures have certain similarities, yet there are significant differences too. The learners also assert that Kazakhs have been affected by globalization. As a result, Kazakhs share beliefs and opinions of American culture in some aspects. The most frequent mistakes in dubbing among the learners were omitting articles, incorrect word order in direct and indirect questions, incorrect use of present perfect and past simple tenses, and word-for-word translation. This work adds to the field of culture, language transfer and using films in the EFL classroom.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".