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Record W2728245844 · doi:10.1017/s002510031700024x

Voice onset time production in Ecuadorian Spanish, Quichua, and Media Lengua

2017· article· en· W2728245844 on OpenAlexaff
Jesse Stewart

Bibliographic record

VenueJournal of the International Phonetic Association · 2017
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVoiceLinguisticsVoice-onset timeLexiconPhonologyHistoryPsychologyGeographyPhilosophy

Abstract

fetched live from OpenAlex

In Ecuador there exists a dynamic language contact continuum between Urban Spanish and Rural Quichua. This study explores the effects of competing phonologies with an analysis of voice onset time (VOT) production in and across three varieties of Ecuadorian highland Spanish, Quichua, and Media Lengua. Media Lengua is a mixed language that contains Quichua systemic elements and a lexicon of Spanish origin. Because of this lexical-grammatical split, Media Lengua is considered the most central point along the language continuum. Native Quichua phonology has a single series of voiceless stops (/ p /, / t /, and / k /), while Spanish shows a clear voicing contrast between stops in the same series. This study makes use of nearly 8,000 measurements from 69 participants to (i) document VOT production in the aforementioned language varieties and (ii) analyse the effects of borrowings on VOT. Results based on mixed effects models and multidimensional scaling suggest that the voicing contrast has entered both Media Lengua and Quichua through Spanish lexical borrowings. However, the VOT values of voiced stops in Media Lengua align with those of Rural and L2 Spanish while Quichua shows significantly longer prevoicing values, suggesting some degree of overshoot.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 science0.0010.000
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 head0.019
GPT teacher head0.319
Teacher spread0.300 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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