MétaCan
Menu
Back to cohort
Record W2801575700 · doi:10.1017/cnj.2018.12

Questioning the role of lexical contrastiveness in phonological development: Converging evidence from perception and production studies

2018· article· en· W2801575700 on OpenAlexaff
Yvan Rose, Sarah Blackmore

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPhonological developmentLinguisticsConsonantPsychologyPerceptionPhonological ruleVocabulary developmentPhonologySyllableContrast (vision)LexiconVocabularyCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In this article, we address relations between lexical and phonological development, with an emphasis on the notion of phonological contrast. We begin with an overview of the literature on word learning and on infant speech perception. Among other results, we report on studies showing that toddlers' perceptual abilities do not correlate with the development of phonological contrasts within their lexicons. We then engage in a systematic comparison between the lexical development of two child learners of English and their acquisition of consonants in syllable onsets. We establish a developmental timeline for each child's onset consonant system, which we compare to the types of phonological contrasts that are present in their expressive vocabularies at each relevant milestone. Like the earlier studies, ours also fails to return tangible parallels between the two areas of development. The data instead suggest that patterns of phonological development are best described in terms of the segmental categories they involve, in relative independence from measures of contrastiveness within the learners' lexicons.

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)0.002
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.303
Teacher spread0.275 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicLanguage Development and DisordersFrench-language works237,207