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Record W1901059773 · doi:10.18800/lexis.200902.002

AMPER-Argentina: pretonemas en oraciones interrogativas absolutas

2009· article· es· W1901059773 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLexis · 2009
Typearticle
Languagees
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInterrogativeLinguisticsProsodyPhraseTone (literature)Intonation (linguistics)Representation (politics)Modality (human–computer interaction)Head (geology)Computer sciencePsychologyNatural language processingArtificial intelligencePhilosophyPolitical science

Abstract

fetched live from OpenAlex

Este trabajo es parte del Proyecto AMPER (Atlas Multimedia de la Prosodia del Espacio Románico). El área dialectal de estudio es el español de Buenos Aires. En el artículo se analizan las oraciones interrogativas absolutas SVO: un SN (núcleos sintácticos paroxítonos, proparoxítonos, oxítonos), un SV (núcleo paroxítono), un SPrep (núcleos paroxítonos, proparoxítonos, oxítonos). También se examinan los pretonemas según el modelo de entonación métrico y autosegmental (AM), y se observa la influencia de la frase fonológica (φ) en la representación fonológica de los acentos tonales. Los resultados de los pretonemas indican diferencias y no un único fraseo prosódico que caracterice a esta modalidad. Los primeros picos (P1) de la primera φ no muestran tonos más altos si se los compara con los P1 de oraciones declarativas. Se descarta un tono de frontera H% inicial. Estos hallazgos confirman otro estudio previo: la información sobre la modalidad interrogativa absoluta se encuentra fuera del pretonema, en el tonema final.

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.

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)0.000
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.276
Teacher spread0.259 · 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