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Record W2096979061 · doi:10.1017/s0305000904006622

Large constituent families help children parse compounds

2005· article· en· W2096979061 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

VenueJournal of Child Language · 2005
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAffect (linguistics)LexiconPsychologyMeaning (existential)ParsingLinguisticsAnalogySegmentationDevelopmental psychologyChemistryNatural language processingCommunicationArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The family size of the constituents of compound words, or the number of compounds sharing the constituents, has been shown to affect adults' access to compound words in the mental lexicon. The present study was designed to see if family size would affect children's segmentation of compounds. Twenty-five English-speaking children between 3;7 and 5;9 were asked to explain the meaning of existing compounds with constituents of varying family size to an alien puppet. The results showed that children were more likely to mention the modifier of compounds if they came from large constituent families than if they came from small constituent families. Other variables were also shown to have some, but smaller effects on children's parsing, including the frequency of the constituent words and the compounds, whether the compounds were already known, and age. These results suggest that children's segmentation of compounds might be facilitated by analogy with other compounds already in their vocabularies.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.268
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