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Record W2140578957 · doi:10.5539/ijel.v5n4p137

Experimental–Phonetic Analysis of Rhythmic Structures in the English Language

2015· article· en· W2140578957 on OpenAlexvenueno aff
Javahir Yagubova

Bibliographic record

VenueInternational Journal of English Linguistics · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsRhythmIntonation (linguistics)Dialogical selfLinguisticsComputer scienceProsodyBasis (linear algebra)Measure (data warehouse)Speech recognitionPsychologyAcousticsMathematicsPhysics

Abstract

fetched live from OpenAlex

The article has been written on the basis of experimental-phonetic method in the study of rhythmic structures in the English language. The main aim of the investigation is to define and systematize the inner laws of the language and to show their role in the formation of rhythmic groups, as well as, to analyse the acoustic parameters and prosodic features of rhythmic structures and interrelation of components in their intonation. For the purpose of investigation we preferred to choose dialogical texts (particularly political talk shows) recorded from American TV Channel CNN and some other dialogues. In order to measure the acoustic parameters of the rhythmical groups the computer program PRAAT has been used.

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)0.003
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.387
Teacher spread0.352 · 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
Published2015
Admission routes1
Has abstractyes

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