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Record W1618712101 · doi:10.1111/modl.12188

Textual Input Enhancement for Vowel Blindness: A Study with Arabic ESL Learners

2015· article· en· W1618712101 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueModern Language Journal · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVowelArabicPsychologyFocus (optics)Decoding methodsLinguisticsBlindnessWord (group theory)AudiologyComputer scienceSpeech recognitionOptometryMedicine

Abstract

fetched live from OpenAlex

This study explores the impact of textual input enhancement on the noticing and intake of English vowels by Arabic L2 learners of English. Arabic L1 speakers are known to experience vowel blindness , commonly defined as a difficulty in the textual decoding and encoding of English vowels due to an insufficient decoding of the word form. Thirty beginner ESL learners participated in a training study during which the experimental group received textual input enhancement on English vowels. Students completed a pretest and an immediate and delayed posttest. An eye‐tracker recorded students' eye fixations during the treatment phase. Results indicate that vowel blindness was significantly reduced for the experimental group who received vowel training in the form of textual input enhancement. This might be due to a longer focus on the target words as suggested by our eye‐tracking data.

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)0.001
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 score0.100
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.389
Teacher spread0.322 · 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