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Record W4409672385 · doi:10.31234/osf.io/cpv3s_v1

Cross-linguistic relations between quantifiers and numerals in language acquisition: Evidence from Japanese

2016· preprint· en· W4409672385 on OpenAlexfundno aff
David Barner, Amanda Libenson, Pierina Cheung, Mayu Takasaki

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsnot available
FundersConnaught FundUniversity of Toronto
KeywordsNumeral systemLinguisticsComputer scienceNatural language processingPsychologyArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

A study of 104 Japanese-speaking 2- to 5-year-olds tested the relationbetween numeral and quantifier acquisition. Experiment 1 assessed Japanesechildren’s comprehension of quantifiers, numerals, and classifiers.Relative to English-speaking counterparts, Japanese children were delayedin numeral comprehension at 2 years old, but showed no difference at 3 and4. Also, Japanese 2-year-olds had better comprehension of quantifiers,indicating that their delay was specific to numerals. A second studyexamined the speech of Japanese and English caregivers, to explore thesyntactic cues that might affect integer acquisition. In English,quantifiers and numerals occurred in similar syntactic positions, andoverlapped to a greater degree than in Japanese. Also, Japanese nouns wereoften dropped, and both quantifiers and numerals exhibited variablepositions relative to the nouns they modified. We conclude that syntacticcues in English facilitate bootstrapping numeral meanings from quantifiermeanings, and that such cues are weaker in classifier languages likeJapanese.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.814

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 communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.314
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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