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Record W4413437409 · doi:10.1017/s0305000925100226

Mandarin-learning toddlers’ sensitivity to noun phrase word order: An investigation of an early bias for language universals

2025· article· en· W4413437409 on OpenAlexaff
Xiaolu Yang, Stella Christie, Rushen Shi

Bibliographic record

VenueJournal of Child Language · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à Montréal
FundersNational Office for Philosophy and Social Sciences
KeywordsWord orderMarkednessNoun phraseLinguisticsPsychologyLinguistic universalMandarin ChineseLanguage acquisitionGrammaticalityDeterminer phrasePhraseNounArtificial intelligenceNatural language processingGrammarComputer science

Abstract

fetched live from OpenAlex

The current study probes Mandarin-learning toddlers' sensitivity to two grammatical noun phrase orders differing in typological markedness. With three visual fixation experiments, we find that by age 2;6, children distinguish the cross-linguistically common order - but not the typologically rare one - from an ungrammatical order; however, their sensitivity to the two grammatical orders does not differ significantly. Further, we conduct a corpus analysis and demonstrate that for early acquisition, both grammatical orders are neither sufficiently nor consistently supported in the linguistic input. The sensitivity patterns and input profile outlined in our study constitute the first step of testing, in a natural language setting, a bias for typologically common ordering discussed in the artificial language learning literature. Although the findings remain inconclusive, they underscore the potential for future investigations in this direction.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.646

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 science0.0000.000
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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

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