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Record W2884368998 · doi:10.1075/rllt.14.19vog

Timing properties of (Brazilian) Portuguese and (European) Spanish

2018· book-chapter· en· W2884368998 on OpenAlexaff
Irene Vogel, Angeliki Athanasopoulou, Natália Brambatti Guzzo

Bibliographic record

VenueRomance languages and linguistic theory · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsTypologyRomance languagesDuration (music)PortugueseLinguisticsSyllableRhythmEuropean PortugueseHistoryArtLiteraturePhilosophy

Abstract

fetched live from OpenAlex

Abstract Linguistic rhythmic or timing categories, usually defined in terms of isochrony, remain controversial as a meaningful typology for classifying languages, despite decades of research. Romance languages offer an opportunity to address this question since closely related languages are proposed to be at different ends of the typology. We test two such languages: European Spanish (ES) and Brazilian Portuguese (BP). Instead of investigating isochrony per se , however, we examine the interface between timing and prominence properties. Since duration is associated with prominence, we test the hypothesis that syllable-timed languages (ES) do not alter duration to express prominence, while non-syllable-timed languages (BP) do. Comparisons of lexical and sentential prominence effects on duration support our hypothesis, confirming the proposed distinction between the rhythmic classes of the two languages.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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