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Record W2992294598

Do mandarin-english bilinguals have an accent in their L1 vowel production?

2008· article· en· W2992294598 on OpenAlexaffvenue
Haisheng Jiang

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMandarin ChineseVowelStress (linguistics)LinguisticsPsychologyPhonetics
DOInot available

Abstract

fetched live from OpenAlex

A study was conducted to examine the Mandarin vowel production by a group of Mandarin-English bilinguals. The study aimed to determine the L1 vowel production of Mandarin-English bilinguals and acoustic properties of the accent. Thirteen Mandarin monolinguals, 33 Mandarin-English bilinguals, and 12 English monolinguals were used for the study to produce vowel tokens. The Mandarin-English bilinguals were divided into a group of high L1 use and a group of low L1 use. The Mandarin monolinguals and Mandarin-English bilinguals produce Mandarin vowel tokens, while the English monolinguals produced English vowel tokens. It was observed that the Mandarin English bilinguals produced sound accented to Mandarin listeners despite of amount of L1 use. The study found that L1 segments need to be included in the speech learning theories.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.336
Teacher spread0.276 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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