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Record W2284842742 · doi:10.1111/bjdp.12137

Thee, uhh disfluency effect in preschoolers: A cue to discourse status

2016· article· en· W2284842742 on OpenAlexafffund
Sarah J. Owens, Susan A. Graham

Bibliographic record

VenueBritish Journal of Developmental Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationUniversity of CalgaryAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsUtterancePsychologyLinguisticsConversationObject (grammar)Mean length of utteranceLanguage developmentDevelopmental psychologyCommunication

Abstract

fetched live from OpenAlex

Speech disfluencies, such as filled pauses (ummm, uhhh), are increasingly recognized as an informative element of the speech stream. Here, we examined whether 2- and 3-year-olds expected that the presence of filled pause would signal reference to objects that are new to a discourse. Children viewed pairs of familiar objects on a screen and heard a speaker refer to one of the objects twice in succession. Next, children heard a critical utterance and were asked to look and point at either the discourse-given (i.e., previously mentioned) or discourse-new (i.e., previously unmentioned) object using a fluent ('Look at the ball!') or disfluent ('Look at thee uh ball!') expression. The results indicated that 3-year-old children, but not 2-year-old children, initially expected the speaker to continue to refer to given information in the critical utterance. Upon hearing a filled pause, however, both 2- and 3-year-old children's looking patterns reflected increased looks to discourse-new objects, although the timing of the effect differed between the age groups. Together, these findings demonstrate that young children have an emerging understanding of the role of filled pauses in speech.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.335
Teacher spread0.324 · 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 teacher head, not a consensus.

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

Citations14
Published2016
Admission routes2
Has abstractyes

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