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Record W2333292330 · doi:10.5539/elt.v9n5p65

Comparison of Iranian Monolingual and Bilingual EFL Students’ Listening Comprehension in Terms of Watching English Movie with Latinized Persian Subtitles

2016· article· en· W2333292330 on OpenAlexvenueno aff
Roghayeh Yamchi

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsPersianPsychologyListening comprehensionLinguisticsComprehensionSignificant differenceActive listeningTest (biology)Neuroscience of multilingualismReading comprehensionCommunicationReading (process)Mathematics

Abstract

fetched live from OpenAlex

<p>The main concern of the present study was to compare Iranian monolingual and bilingual EFL students’ listening comprehension in terms of Latinized Persian subtitling of English movie to see whether there was a significant difference between monolinguals and bilinguals on immediate linguistic comprehension of the movie. Latinized Persian subtitling was representing Persian language in Latin script. To achieve this end, an ex post facto design was employed. The homogenized participants of this study were 24 Persian monolingual students and 22 Azeri-Persian bilingual students. One listening comprehension test which was based on the linguistic information of the movie was administered to both groups of monolinguals and bilinguals. The results of Mann-Whitney U test revealed a significant difference between two groups; that is, monolinguals outperformed bilinguals on immediate linguistic comprehension of the movie. Finally, the study concludes with some pedagogical implications and recommendations for further research.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.302
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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