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Record W2137449983 · doi:10.1017/s0305000915000458

Children's identification of questions from rising terminal pitch

2015· article· en· W2137449983 on OpenAlexaff
Mathieu R. Saindon, Sandra E. Trehub, E. Glenn Schellenberg, Pascal van Lieshout

Bibliographic record

VenueJournal of Child Language · 2015
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyTerminal (telecommunication)Identification (biology)LinguisticsDevelopmental psychologyCommunicationTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Young children are slow to master conventional intonation patterns in their yes/no questions, which may stem from imperfect understanding of the links between terminal pitch contours and pragmatic intentions. In Experiment 1, five- to ten-year-old children and adults were required to judge utterances as questions or statements on the basis of intonation alone. Children eight years of age or younger performed above chance levels but less accurately than adult listeners. To ascertain whether the verbal content of utterances interfered with young children's attention to the relevant acoustic cues, low-pass filtered versions of the same utterances were presented to children and adults in Experiment 2. Low-pass filtering reduced performance comparably for all age groups, perhaps because such filtering reduced the salience of critical pitch cues. Young children's difficulty in differentiating declarative questions from statements is not attributable to basic perceptual difficulties but rather to absent or unstable intonation categories.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.313
Teacher spread0.302 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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