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Record W2016083184 · doi:10.1121/1.1429202

Acoustic-phonetic description of infant speech samples: coding reliability and related methodological issues

2002· article· en· W2016083184 on OpenAlexaff
Susan Rvachew, Dianne Creighton, N Feldman, Reg Sauvé

Bibliographic record

VenueAcoustics Research Letters Online · 2002
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsAlberta Children's HospitalMcGill University
Fundersnot available
KeywordsIntraclass correlationInter-rater reliabilityPhonationAudiologyIntra-rater reliabilityPsychologyKappaReliability (semiconductor)Speech recognitionUtteranceStatisticsMathematicsComputer scienceMedicineDevelopmental psychologyPsychometrics

Abstract

fetched live from OpenAlex

Two samples of speech-like vocalizations were recorded from each of 18 infants who were 8, 12, or 18 months of age at the time of recording. The two samples were recorded on different days, with less than one week between recordings. Each utterance was coded as belonging to one of several possible infraphonological categories, and canonical syllable ratios were determined for each sample. Syllables produced with abnormal phonation were identified. These coding procedures were completed independently by two raters. One sample from each infant was coded twice by the same rater. This sequence of multiple recordings and repeat analyses allowed for the determination of interrater, intrarater, and test-retest coding reliability. Kappa and intraclass correlation analyses revealed excellent reliability for all measures.

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)0.075
metaresearch head score (Gemma)0.183
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.183
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.225
GPT teacher head0.430
Teacher spread0.205 · 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.

Study designObservational
DomainMethods
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

Citations9
Published2002
Admission routes1
Has abstractyes

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