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Record W4311697079 · doi:10.31234/osf.io/4tahj

The Development of Abstract Word Meanings

2022· preprint· en· W4311697079 on OpenAlexafffund
Emiko J. Muraki, Lorraine Dale Reggin, Carissa Y. FEDDEMA, Penny M. Pexman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConcretenessPsychologyMeaning (existential)LinguisticsWord (group theory)Age of AcquisitionSemantics (computer science)Cognitive psychologyComputer scienceCognitionPhilosophy

Abstract

fetched live from OpenAlex

Extensive research has shown that children’s early words are learned through sensorimotor experience. Thus, early-acquired words tend to have more concrete meanings. Abstract word meanings tend to be learned later but less is known about their acquisition. We collected meaning-specific concreteness ratings and examined their relationship with age-of-acquisition. Earlier-acquired meanings were rated as more concrete while later-acquired meanings as more abstract, particularly for words typically considered to be concrete. The results suggest that sensorimotor experiences are important to early-acquired word meanings, and other experiences (e.g., linguistic) are important to later-acquired meanings, consistent with a multi-representational view of lexical semantics.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.000

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 head0.039
GPT teacher head0.321
Teacher spread0.282 · 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 teacher head, not a consensus.

Study designOther design
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

Citations1
Published2022
Admission routes2
Has abstractyes

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