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Record W3114822848 · doi:10.47393/jshe.v10ispecial.903

INTERACTION BETWEEN INTONATION AND LEXICAL TONE IN SOUTHERN VIETNAMESE DISCOURSE MARKERS

2020· article· en· W3114822848 on OpenAlexaff
Thị Thu Hà Phạm, Marc Brunelle

Bibliographic record

VenueUED Journal of Social Sciences Humanities and Education · 2020
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Ottawa
FundersAustralian National UniversityUniversity of Cambridge
KeywordsVietnameseIntonation (linguistics)Tone (literature)LinguisticsPsychologySalientComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

This study based on Southern Vietnamese (SVN) task-oriented dialogues, aims to find evidences of the present of intonation as a grammatical category, as well as the interaction between intonation and lexical tone in monosyllable discourse utterances (discourse markers). The results show that there is little evidence for phonologized boundary tones in SVN discourse markers. This finding would strengthen the claim made by Brunelle et al. (2012) (focusing on Northern Vietnamese) that Vietnamese intonation may not be grammaticalized as it could be in non-tonal 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: 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
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.110
GPT teacher head0.442
Teacher spread0.331 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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