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Record W2546652767 · doi:10.20396/liames.v0i14.1522

La ambigüedad fonológica de los segmentos complejos:

2014· article· es· W2546652767 on OpenAlexaff
Analía Gutiérrez

Bibliographic record

VenueLIAMES Línguas Indígenas Americanas · 2014
Typearticle
Languagees
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

El objetivo de este trabajo es analizar el comportamiento fonológico del segmento complejo [k͡l] en la lengua nivaĉle (mataco-mataguaya). Este sonido presenta interesantes particularidades tipológicas y teóricas ya que: (a) en lo que a mi conocimiento respecta, la [k͡l] no está presente en otras lenguas indígenas de América del Sur, (b) esta consonante es una africada no-homorgánica que tiene dos articuladores: dorsal y coronal (Stell 1989:58) y (c) no hay una sonorante lateral en el inventario fonológico de la lengua nivaĉle – la otra consonante lateral es la lateral fricativa sorda [ɬ]. Los datos comparativos sugieren que la consonante nivaĉle [k͡l] corresponde a la sonorante lateral [l] en otras lenguas mataco-mataguayas (chorote, maká y wichí). Con respecto a las propiedades distribucionales de la consonante [k͡l], ésta sólo ocurre en posición de ataque; en posición de coda aparece como una [k]. Por un lado, planteo la hipótesis que la [k͡l] es el resultado diacrónico de un proceso fonológico de fortalecimiento (fortition/hardening) de la sonorante lateral [l] en una posición prosódica fuerte, como es el ataque. Por otro lado, el hecho que [k͡l] delateraliza y emerge como [k], y no como [l] en posición de coda, pareciera demostrar que su representación sincrónica subyacente es /k͡l/. Este trabajo, entonces, espera contribuir a la descripción y comprensión de la fonología nivaĉle tanto a nivel segmental como prosódico.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.018
GPT teacher head0.359
Teacher spread0.341 · 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 designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

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