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Record W1968231654 · doi:10.1017/s0142716409990075

Gender differences in language development in French Canadian children between 8 and 30 months of age

2009· article· en· W1968231654 on OpenAlexafffundabout
Caroline Bouchard, Natacha Trudeau, Ann Sutton, MARIE-CLAUDE BOUDREAULT, Joane Deneault

Bibliographic record

VenueApplied Psycholinguistics · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à RimouskiUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyNormativeDevelopmental psychologyLanguage developmentLanguage acquisitionLinguisticsMathematics education

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of this article is to examine the language of girls and boys between 8 and 30 months of age, using the Quebec French version of The MacArthur Communicative Development Inventories. The findings from this parental report measure confirm those of earlier research, which showed the linguistic superiority of girls over boys at a young age. More specifically, the results show that girls produce significantly more words than boys; their utterances contain a greater number of grammatical forms, and are more complex syntactically. On the qualitative level, the data illustrate distinctive characteristics associated with gender in the acquisition of the first 100 words. These findings suggest that caution is necessary when assessing young children to interpret performance in light of factors that may contribute to it, including gender. These results are discussed in light of whether separate normative data are warranted for young boys and girls learning Canadian French.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.205
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.285
Teacher spread0.263 · 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

Citations91
Published2009
Admission routes3
Has abstractyes

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