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Record W2968628572 · doi:10.1017/s0305000919000369

Preschoolers are sensitive to accent distance

2019· article· en· W2968628572 on OpenAlexaff
Drew Weatherhead, Ori Friedman, Katherine S. White

Bibliographic record

VenueJournal of Child Language · 2019
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
Fundersnot available
KeywordsStress (linguistics)PsychologyProsodyUtteranceLinguisticsContrast (vision)Variation (astronomy)

Abstract

fetched live from OpenAlex

Can children tell how different a speaker's accent is from their own? In Experiment 1 (N = 84), four- and five-year-olds heard speakers with different accents and indicated where they thought each speaker lived relative to a reference point on a map that represented their current location. Five-year-olds generally placed speakers with stronger accents (as judged by adults) at more distant locations than speakers with weaker accents. In contrast, four-year-olds did not show differences in where they placed speakers with different accents. In Experiment 2 (N = 56), the same sentences were low-pass filtered so that only prosodic information remained. This time, children judged which of five possible aliens had produced each utterance, given a reference speaker. Children of both ages showed differences in which alien they chose based on accent, and generally rated speakers with foreign accents as more different from their native accent than speakers with regional accents. Together, the findings show that preschoolers perceive accent distance, that children may be sensitive to the distinction between foreign and regional accents, and that preschoolers likely use prosody to differentiate among accents.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

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)0.0010.001

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 head0.008
GPT teacher head0.318
Teacher spread0.310 · 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 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

Citations20
Published2019
Admission routes1
Has abstractyes

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