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Record W2922295638 · doi:10.5539/elt.v12n4p115

The Improvement of Intelligibility in the Oral Production of Standard English: A Study About the Production of Vowel Quality in Stressed and Unstressed Syllables

2019· article· en· W2922295638 on OpenAlexvenueno aff
Ana María Muñoz Mallén, Víctor Pavón Vázquez

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsIntelligibility (philosophy)PronunciationVowelPsychologyLinguisticsPhonologyAudiology

Abstract

fetched live from OpenAlex

Pronunciation is an essential aspect in the teaching of the English language, especially those aspects of pronunciation such as stress and vowel quality as they are crucial elements to ensure intelligibility in communication. The general objective of this study is to investigate whether the theoretical-practical instruction on pronunciation has a crucial impact on the vowel quality production of stressed and unstressed syllable in isolated words and in wider contexts, and therefore, in the improvement of intelligibility and of the oral production in general terms, in two groups of Spanish students of English (the control and the experimental group). More particularly, the study addresses the impact of formal instruction in pronunciation based on deduction in terms of rule formation from a cognitive perspective. The results indicate that the specific work implemented with the production of vowel quality in stressed and unstressed syllables have a significant impact on intelligibility.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.365
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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