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Record W2157647232 · doi:10.1177/0142723713508869

Preschool children conflate pragmatic agreement and semantic truth

2013· article· en· W2157647232 on OpenAlexaff
David R. Olson, Janet Wilde Astington

Bibliographic record

VenueFirst Language · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFalsityPragmaticsConflationPsychologyLinguisticsSemantics (computer science)Expression (computer science)Object (grammar)Cognitive psychologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Children’s ability to ascribe beliefs to themselves and others has been shown to develop in the late preschool and early school years. This ability to represent, that is to think about, beliefs known to be false is described as metarepresentational development. This article extends these findings to the domain of linguistic representations by exploring how children come to judge statements as true or false of a given reality. It presents two models of judgment, one based on the pragmatics of the expression, the other on the basis of the semantics of the expression. Two studies show that when preschoolers object to what someone has said, they are disagreeing with the speaker, a pragmatic judgment, rather than judging the truth or falsity of what the speaker has said, a semantic judgment. True/false judgments are metalinguistic judgments, later acquired, suggesting a new consciousness of linguistic form.

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)0.004
metaresearch head score (Gemma)0.047
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 score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.047
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.006
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Citations12
Published2013
Admission routes1
Has abstractyes

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