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Do Early Nouns Refer to Kinds or Distinct Shapes?

2009· article· en· W2165126084 on OpenAlex

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

VenuePsychological Science · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyObject (grammar)NounWord (group theory)Word learningLinguisticsCognitive psychologyCommunicationVocabulary

Abstract

fetched live from OpenAlex

What is the nature of early words? Specifically, do infants expect words for objects to refer to kinds or to distinct shapes? The current study investigated this question by testing whether 10-month-olds expect internal object properties to be predicted by linguistic labels. A looking-time method was employed. Infants were familiarized with pairs of identical or different objects that made identical or different sounds. During test, before the sounds were demonstrated, paired objects were labeled with one repeated count-noun label or two distinct labels. Results showed that infants expected objects labeled with distinct labels to make different sounds and objects labeled with repeated labels to make identical sounds, regardless of the objects' appearance. These findings indicate that the 10-month-olds' expectations about internal properties of objects were driven by labeling and provide evidence that even at the beginning of word learning, infants expect distinct labels to refer to different kinds.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.005

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 head0.066
GPT teacher head0.405
Teacher spread0.339 · 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