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Concept formation and language development: count nouns and object kinds

2012· book-chapter· en· W2663308242 on OpenAlexaff
Fei Xu

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCategorizationNounObject (grammar)LinguisticsInferenceGrammatical categoryPsychologyIndividuationConcept learningComputer scienceCognitive scienceArtificial intelligenceCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract What is the role of language in concept formation in infancy? More specifically, does learning count nouns play a causal role in infants' acquisition of object kind concepts? The relationship between language development and concept formation is an old question that has intrigued psycholinguists, philosophers, and psychologists for decades. Recent years have witnessed a surge of interest in this area. Many domains of conceptual representations and the corresponding linguistic representations have been investigated. At the syntactic level, researchers have explored the role of morphsyntax in spatial representations as well as the understanding of false belief. At the lexical level, they have explored how words of various grammatical classes may impact on spatial category formation, number representations, and representations of objects and substances. This article focuses on a universal aspect of language: representations of count nouns that refer to object kinds. It discusses count nouns and categorization, individuation, and inductive 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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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 designTheoretical or conceptual
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

Citations2
Published2012
Admission routes1
Has abstractyes

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Same venueOxford University Press eBooksSame topicChild and Animal Learning DevelopmentFrench-language works237,207