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Record W2789562045 · doi:10.3765/amp.v5i0.4239

Gradient and Categorical Effects in Native and Non-native Nasal-rhotic Coordination

2018· article· en· W2789562045 on OpenAlexafffundabout
Alexei Kochetov, Laura Colantoni, Jeffrey Steele

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2018
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsCoarticulationAssimilation (phonology)LinguisticsConsonantArticulation (sociology)Place of articulationFirst languageSecond languagePsychologySecond-language acquisitionComputer scienceVowel

Abstract

fetched live from OpenAlex

Languages are known to differ in their patterns of consonant-to-consonant coordination. Acquisition of a second language (L2) therefore involves learning these language-specific coordination patterns and the corresponding coarticulation and assimilation processes within and across words. This paper seeks to determine whether L2 learners of English acquire the target pattern of gradient assimilation of the nasal + rhotic sequence in English (e.g. in Rome). Electropalatographic data were obtained from nine learners of English (native speakers of French, Japanese, and Spanish) and three Canadian English controls. The results revealed that, although the learners had largely acquired the English rhotic articulation, most of them (Japanese and Spanish speakers, in particular) had not fully mastered the target C-C coordination patterns. This is consistent with findings of previous acoustic studies of L2 timing and coarticulation, highlighting the difficulty of acquiring gradient phonetic phenomena.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes3
Has abstractyes

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