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Record W2002807283 · doi:10.1177/1367006910370918

What is the impact of age of second language acquisition on the production of consonants and vowels among childhood bilinguals?

2010· article· en· W2002807283 on OpenAlexaff
Andrea A. N. MacLeod, Carol Stoel‐Gammon

Bibliographic record

VenueInternational Journal of Bilingualism · 2010
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologyVoice-onset timeFormantNeuroscience of multilingualismLinguisticsAge of AcquisitionContrast (vision)Dominance (genetics)Speech productionVowelComputer scienceCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigated bilinguals’ ability to produce language-specific acoustic values for consonants and vowels that are highly similar across the two languages. To investigate this ability, we targeted early bilinguals who had acquired two languages before the age of 12 and continued to use both languages on a daily basis. These adult bilinguals were separated into two groups: simultaneous bilinguals (or nearly so) who acquired both languages by their third year, and sequential bilinguals who acquired their second language between the ages of 8 and 12 years. Their speech production was studied through an acoustic analysis of stop consonants (voice onset time) and vowels (formant structure). Despite the differences in age of acquisition, these bilinguals used both languages on a regular basis at work and at home and were very proficient in both languages. In contrast to other early bilinguals who undergo a change in language dominance from their first language to their second, the participants in this study maintained relatively balanced abilities in both languages. This study revealed that childhood bilinguals can maintain contrasts across their two languages, even for very similar phonemes.

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)0.001
metaresearch head score (Gemma)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.019
GPT teacher head0.378
Teacher spread0.359 · 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 designBench or experimental
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

Citations29
Published2010
Admission routes1
Has abstractyes

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