MétaCan
Menu
Back to cohort
Record W2504808664 · doi:10.1075/tilar.7.06kid

Learning the meaning of “um”

2011· book-chapter· en· W2504808664 on OpenAlexaff
Celeste Kidd, Katherine S. White, Richard Ν. Aslin

Bibliographic record

VenueTrends in language acquisition research · 2011
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMeaning (existential)PsychologyLinguisticsSociologyPhilosophyPsychotherapist

Abstract

fetched live from OpenAlex

Previous research has uncovered various contextual and social cues that children may use to infer speakers' communicative intentions (e.g. joint visual attention, pointing). We review evidence from eye-tracking studies that suggests that by 2;6 years of age, children use another previously unexplored cue to infer speakers' communicative intentions: speech disfluencies. Disfluencies (e.g. “uh” and “um”) often occur before unfamiliar, infrequent, and discourse-new words. Thus, disfluencies provide information about a speaker's intended referent. Further children use the presence of a disfluency before an object label to anticipate a novel, discourse-new referent. These results demonstrate that children go beyond their input, acquiring the generalization that disfluencies precede not just specific words, but rather categories of words that are difficult to produce. Keywords: Language acquisition; speech disfluencies; lexical development; eye-tracking; attention

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)0.001
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.079
GPT teacher head0.394
Teacher spread0.315 · 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

Citations20
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueTrends in language acquisition researchSame topicLanguage Development and DisordersFrench-language works237,207