MétaCan
Menu
Back to cohort
Record W2799417675 · doi:10.5070/p74cs2g297

The A-map Model: Articulatory Reliability in Child-specific Phonology

2013· article· en· W2799417675 on OpenAlexaff
Tara McAllister Byun, Sharon Inkelas, Yvan Rose

Bibliographic record

VenueUC Berkeley Phonology Lab Annual Reports · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPhonologyPronunciationGrammarLinguisticsPhenomenonComputer sciencePsychologyPhonological ruleCognitive psychologyProcess (computing)Range (aeronautics)Speech productionCompetition modelSpeech recognitionEngineering

Abstract

fetched live from OpenAlex

This article addresses a phenomenon of long-standing interest: the existence of child-specific phonological patterns that are not attested in adult language. We propose a new theoretical approach, termed the A(rticulatory )-map model, to account for the origin and elimination of child-specific phonological patterns. Due to the performance limitations imposed by structural and motor immaturity, children’s outputs differ from adult target forms in both systematic and sporadic ways. The computations of the child’s grammar are influenced by the distributional properties of motor-acoustic traces of previous productions, stored in episodic memory and indexed in the eponymous A-map. We propose that child phonological patterns are shaped by competition between two essential forces: the pressure to match adult productions of a given word (even if the attempt is likely to fail due to performance limitations), and the pressure to attempt a pronunciation that can be realized reliably (even if phonetically inaccurate). These forces are expressed in the grammar by two constraints that draw on the motor-acoustic detail stored in the A-map. These constraints are not child-specific, but remain present in the adult grammar, although their influence is greatly attenuated as a wide range of motor plans come to be realized with a similar degree of reliability. The A-map model thus not only offers an account of a problematic phenomenon in development, but also provides a mechanism to model motor-grammar interactions in adult speech, including in cases of acquired speech impairment.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueUC Berkeley Phonology Lab Annual ReportsSame topicLanguage Development and DisordersFrench-language works237,207