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Record W2963135801 · doi:10.5539/elt.v12n8p89

Review of Chinese English Learners’ Prosodic Acquisition

2019· article· en· W2963135801 on OpenAlexvenueno aff
Yan Wu

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsProsodyPronunciationPsychologyLinguisticsStress (linguistics)Intonation (linguistics)SyllableMandarin ChineseFirst languageNaturalness

Abstract

fetched live from OpenAlex

The traditional focus of English phonetic teaching in China has consistently been on the segmental acquisition, which is mainly highlighting the pronunciation of vowels and consonants, while its suprasegmental knowledge in speech naturalness, coherence and understanding is relatively insufficient. In addition, Chinese students have a serious problem in the rhythm of English language, which is mainly influenced by the characteristics of the syllable-timed in their mother tongue rather than in a stress-timed way. This study reviews the academic works of the nearly 15 years in the development of Chinese prosodic features of English language, giving a better and deeper analysis and appreciation of the suprasegmental phoneme levels of different aspects, such as the fundamental components of English prosody such as stress, rhythm and intonation. This study is hoped to shed light on the prosodic analysis of Chinese English learners’ oral proficiency in pronunciation, finding out the insufficiency in prosody of China English, and more importantly to provide effective learning strategy for Chinese English learners and teachers in prosody acquisition, therefore, it might pave the way to the reform of oral English teaching in China.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0060.000

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 head0.010
GPT teacher head0.330
Teacher spread0.320 · 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.

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

Citations12
Published2019
Admission routes1
Has abstractyes

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