INTERACTIONS AND TEXT PRODUCTION: BENEFITS FOR BOYS AND GIRLS
Bibliographic record
Abstract
Learning to write is challenging for elementary school pupils, particularly boys, who show poorer writing performance than girls (Herbert & Stipek, 2005, MELS, 2012).Offering pupils motivating and meaningful writing activities thus represents a significant challenge for teachers (Colognesi & Lucchini, 2018).Since boys generally enjoy interacting with their peers, why not take advantage of this interest and allow them to write in pairs?When this opportunity is given to them, what kinds of spoken exchanges occur between them?Do these exchanges differ from those of girls?And are the texts produced in pairs of better quality?To date, few studies have compared boys' interactions with those of girls or the impact of these interactions on the quality of the texts produced.The aim of this study was thus to 1) describe the content of the interactions of girls and boys in Grade 6 (11-12 years old) when producing texts in dyads and 2) compare the quality of the texts produced by these pupils according to the writing context (individually and in dyads).Thirty-three (33) dyads participated in this study (N = 66, 35 girls and 31 boys).The pupils planned, wrote and edited/corrected a story individually and then in dyads.Their writing performance (syntax, punctuation, vocabulary, narrative structure, lexical and grammatical spelling) and interactions (number, content) were evaluated and compared.The results are presented and discussed in light of the benefits of collaborative writing activities for boys and girls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".