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Record W3161430739 · doi:10.1177/00238309211013864

Comparing Phonetic Convergence in Children and Adults

2021· article· en· W3161430739 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLanguage and Speech · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsImitationStress (linguistics)PsychologyMandarin ChineseVoice-onset timeLinguisticsSocial psychologyPerception

Abstract

fetched live from OpenAlex

Observations by sociolinguists suggest that when children relocate to a new community, they rapidly learn to imitate their peers, adopting the new local accent faster and more effectively than adults. However, few well-controlled laboratory experiments have been conducted comparing speech or accent imitation across ages. Here, we investigated Canadian English-speaking children's and adults' imitation of three model speakers: a Canadian English talker, an Australian English talker, and a non-native Mandarin English talker who learned English later in life. The speech of all three talkers was manipulated to have elongated voice onset time (VOT) on word initial stop consonants. The dependent measure was how much participants would lengthen their VOTs after exposure to one of the talkers in two paradigms: delayed shadowing (Experiment 1) and immediate shadowing (Experiment 2). We predicted that overall children would show more imitation than adults, particularly when imitating the Canadian English talker, given previous work on children's social preferences. Although we did not observe age differences in either study, when shadowing was immediate, we found that imitation was influenced by the accent of the speaker, but not in the manner we predicted: both age groups imitated the Mandarin-accented model more strongly than the Canadian model. When shadowing was delayed, we observed no evidence of imitation. We discuss our findings in light of other recent work, and conclude that the development of speech imitation is an area ripe for further investigation.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.045
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.303
Teacher spread0.289 · 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