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Record W1998425326 · doi:10.5539/ells.v1n1p2

Using Subtitles to Enliven Reading

2011· article· en· W1998425326 on OpenAlex
Yanling Hwang, Pei-Wen Huang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Language and Literature Studies · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsClosed captioningReading comprehensionReading (process)Computer scienceComprehensionTest (biology)MultimediaMathematics educationLinguisticsPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

There are an increasing number of foreign language teaching techniques that integrate with the latest technology, such as computers, video materials. As the emphasis in multimedia shifts to success for all language learners, educators tend to carry out various techniques to demonstrate benefits. Presenting subtitles aids visual channels to communicate verbal information. The presenter examines whether video English captions improve or impede EFL students’ reading comprehension.Using the instructional videos with English subtitles 1hour every 2 weeks over 10 weeks. Two versions of videos, one with captioning and one without it, were used by two groups randomly selected among freshmen at the university in Taiwan. Reading comprehension test- General English Proficiency Test (GEPT) – intermediate- was administered by participants in order to determine the influences of using subtitles on learners’ reading comprehension.

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.

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 score0.166
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.084
GPT teacher head0.298
Teacher spread0.214 · 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