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Record W2136759646 · doi:10.1080/09500782.2010.502968

Writing attainment in 9- to 11-year-olds: some differences between girls and boys in two genres

2010· article· en· W2136759646 on OpenAlexaff
Roger Beard, Andrew Burrell

Bibliographic record

VenueLanguage and Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsEducation and Early Childhood Development
FundersAstellas Pharma US
KeywordsPsychologyEducational attainmentDevelopmental psychologyLinguisticsPolitical science

Abstract

fetched live from OpenAlex

Gender differences in the imaginative narrative and persuasive description writing of a sample of Year 5 (9- to 10-year-old) children were investigated using a standardised test and a repeat design, with the same tasks being undertaken a year later. The texts were analysed using test guidelines and genre-specific rating scales derived from the relevant literature. Differences in writing attainment were found to exist, with boys generally performing less well than girls. In the five constituents of writing assessed by the test, girls scored significantly higher in four in both years. Boys did not score significantly higher than girls in any constituent in either year. However, boys wrote significantly more in Year 6 than they had written in Year 5, and this may reflect increases in handwriting attainment. Boys’ under-attainment was less pronounced in the persuasive description writing, and they scored significantly higher than girls in Year 5 in three features of this writing. Although a subgroup of the highest-attaining children contained more girls than boys, a detailed analysis did not indicate any girl–boy differences in textual effectiveness, content or language use. Some possible implications for practice and suggestions for further research are provided.

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)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.358
Teacher spread0.342 · 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

Citations44
Published2010
Admission routes1
Has abstractyes

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