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Record W2013574145 · doi:10.1155/2013/298603

A Bidirectional Relationship between Conceptual Organization and Word Learning

2013· article· en· W2013574145 on OpenAlexaff
Tanya Kaefer, Susan B. Neuman

Bibliographic record

VenueChild Development Research · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsLakehead University
FundersInstitute of Education Sciences
KeywordsCategorizationWord (group theory)PsychologyControl (management)Concept learningWord learningsortDevelopmental psychologyCognitive psychologyNatural language processingArtificial intelligenceLinguisticsComputer scienceVocabularyInformation retrieval

Abstract

fetched live from OpenAlex

This study explores the relationship between word learning and conceptual organization for preschool-aged children. We proposed a bidirectional model in which increases in word learning lead to increases in taxonomic organization, which, in turn, leads to further increases in word learning. In order to examine this model, we recruited 104 4-year olds from Head Start classrooms; 52 children participated in a two-week training program, and 52 children were in a control group. Results indicated that children in the training program learned more words and were more likely to sort taxonomically than children in the control condition. Furthermore, the number of words learned over the training period predicted the extent to which children categorized taxonomically. Additionally, this ability to categorize taxonomically predicted the number of words learned outside the training program, over and above the number of words learned in the program. These results suggest a bi-directional relationship between conceptual organization and word learning.

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 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.100
GPT teacher head0.362
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2013
Admission routes1
Has abstractyes

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