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Record W2504808664 · doi:10.1075/tilar.7.06kid

Learning the meaning of “um”

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1140.001

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 head0.079
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