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Record W2086022490 · doi:10.1111/cdev.12185

Referential Labeling Can Facilitate Phonetic Learning in Infancy

2013· article· en· W2086022490 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.

Bibliographic record

VenueChild Development · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAttunementPsychologyTone (literature)Contrast (vision)Object (grammar)PerceptionVocabularyLinguisticsCognitive psychologyVocabulary developmentWord learningSpeech perceptionPhoneticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

All languages employ certain phonetic contrasts when distinguishing words. Infant speech perception is rapidly attuned to these contrasts before many words are learned, thus phonetic attunement is thought to proceed independently of lexical and referential knowledge. Here, evidence to the contrary is provided. Ninety-eight 9-month-old English-learning infants were trained to perceive a non-native Cantonese tone contrast.Two object–tone audiovisual pairings were consistently presented, which highlighted the target contrast (Object A with Tone X; Object B with Tone Y). Tone discrimination was then assessed. Results showed improved tone discrimination if object–tone pairings were perceived as being referential word labels, although this effect was modulated by vocabulary size. Results suggest how lexical and referential knowledge could play a role in phonetic attunement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.998

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)0.0060.003

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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