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Record W3199177254 · doi:10.1515/opli-2020-0169

A preliminary analysis of intonation patterns in Ecuadorian Cuencano Spanish

2021· article· en· W3199177254 on OpenAlexaff
Alex Jamil Portocarrero, Jesse Stewart

Bibliographic record

VenueOpen Linguistics · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIntonation (linguistics)LinguisticsSingingUtteranceStress (linguistics)PsychologyPitch accentHistoryProsodyPhilosophy

Abstract

fetched live from OpenAlex

Abstract El Cantado Cuencano“Cuencano singing” constitutes the hallmark of Cuencano Spanish: a widely spoken Andean dialect in the Ecuadorian province of Azuay. This colloquially described “singing” makes Cuencano Spanish one of the most distinct dialects of Ecuador. The aim of the present study is to provide a preliminary analysis of intonation patterns from common utterance types in this under documented dialect. A sample of 550 utterances from 11 categories that included declarative statements, yes/no questions, exclamative statements, wh-questions, imperatives, lists, conditionals, tag-questions, interjections, negative statements, and vocatives was collected from five male and five female participants. The tokens were analyzed acoustically and labeled using the Spanish Tones and Break Indices system (Sp_ToBI). Results reveal the presence of a tritonal pitch accent (PA), labeled as L + H* + L and the extensive use of bitonal PAs (namely, L + H*). These three phenomena mark the singing quality of Cuencano Spanish and make it stand out from the dialects of Ecuadorian Spanish.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.405
Teacher spread0.353 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes1
Has abstractyes

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