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Record W2148830769 · doi:10.1177/0142723715579604

Asking for action or information? Crosslinguistic comparison of interrogative functions in early child Cantonese and Mandarin

2015· article· en· W2148830769 on OpenAlexfundno aff
Hui Li, Eileen Wong, SK Tse, Shing On Leung, Qianling Ye

Bibliographic record

VenueFirst Language · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
FundersEnvironment and Climate Change Canada
KeywordsInterrogative wordInterrogativeMandarin ChineseLinguisticsPsychologyAction (physics)TurkishPhilosophy

Abstract

fetched live from OpenAlex

Request for information (RfI) is believed to be the universally dominant function of young children’s questioning, whereas request for action (RfA) has been reported to be the leading interrogative form used in early child Cantonese. The possibility of crosslinguistic variability prompts further research and comparison with additional languages. This study compares the interrogatives elicited from two early Chinese language corpora: Early Childhood Cantonese Corpus (ECCC) and Early Childhood Mandarin (ECMC). Altogether, 1214 and 942 question types were elicited from ECCC and ECMC, respectively. Analyses indicated that: (1) all the interrogative functions identified in an earlier study of Cantonese were also observed in the early Mandarin interrrogatives; and (2) both RfA (49.9%) and RfI (45.5%) were the most frequently observed functions of early child Chinese interrogatives. This crosslinguistic evidence suggests that follow-up studies are needed to further explore the possible influences of language, culture and communication tasks on children’s uses of interrogative forms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.388
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2015
Admission routes1
Has abstractyes

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