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Record W2031023832 · doi:10.1017/s0305000911000018

Speaker reliability in preschoolers' inferences about the meanings of novel words

2011· article· en· W2031023832 on OpenAlexaff
David M. Sobel, Julie Sedivy, D. W. Buchanan, Rachel Hennessy

Bibliographic record

VenueJournal of Child Language · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyInferenceTask (project management)Object (grammar)Competence (human resources)Cognitive psychologyLinguisticsDevelopmental psychologySocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Preschoolers participated in a modified version of the disambiguation task, designed to test whether the pragmatic environment generated by a reliable or unreliable speaker affected how children interpreted novel labels. Two objects were visible to children, while a third was only visible to the speaker (a fact known by the child). Manipulating whether a novel object was visible to both interlocutors or hidden from the child tested the child's understanding of pragmatic expectations of interlocutor competence. When interacting with a speaker with a history of accurately labeling familiar objects, children responded appropriately in both cases. When interacting with a speaker who previously generated inaccurate labels for familiar objects, children's behavior and eye-movements reflected their belief that the speaker was not a competent communicator. These data support the hypothesis that children consider the pragmatic environment constructed by an interlocutor when that speaker asks them to make a lexical inference.

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)0.006
metaresearch head score (Gemma)0.042
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 score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.042
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.275
Teacher spread0.229 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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