MétaCan
Menu
Back to cohort
Record W2168838199 · doi:10.1177/0023830908099068

Child—Adult Differences in Second-Language Phonological Learning: The Role of Cross-Language Similarity

2008· article· en· W2168838199 on OpenAlexaff
Wendy Baker, Pavel Trofimovich, James Emil Flege, Molly Mack, Randall Halter

Bibliographic record

VenueLanguage and Speech · 2008
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsConcordia University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsVowelPsychologyPerceptionPhonological developmentLinguisticsSimilarity (geometry)Second languageFirst languageSpeech productionLanguage acquisitionMid vowelSpeech perceptionAudiologyCognitive psychologyPhonologyComputer scienceArtificial intelligenceFormantMedicine

Abstract

fetched live from OpenAlex

This study evaluated whether age effects on second language (L2) speech learning derive from changes in how the native language (L1) and L2 sound systems interact. According to the "interaction hypothesis" (IH), the older the L2 learner, the less likely the learner is able to establish new vowel categories needed for accurate L2 vowel production and perception because, with age, L1 vowel categories become more likely to perceptually encompass neighboring L2 vowels. These IH predictions were evaluated in two experiments involving 64 native Korean- and English-speaking children and adults. Experiment 1 determined, as predicted, that the Korean children were less likely than the Korean adults to perceive L2 vowels as instances of a single L1 vowel category. Experiment 2 showed that the Korean children surpassed the Korean adults in production of certain vowels but equaled them in vowel perception. These findings, which partially support the IH, are discussed in relation to L2 speech learning.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0010.001
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 head0.020
GPT teacher head0.321
Teacher spread0.300 · 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

Citations120
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueLanguage and SpeechSame topicPhonetics and Phonology ResearchFrench-language works237,207