Des textes identitaires plurilingues pour stimuler l’engagement dans l’écriture d’élèves immigrants allophones en situation de grand retard scolaire au secondaire
Bibliographic record
Abstract
Learning to write in a second language is an enormous challenge, particularly for undereducated allophone immigrant students. In this context, interventions fostering students’ engagement in writing appear to be a promising way to contribute to their learning process ( Cummins, 2009 ). Engagement in writing would be promoted by taking into account students’ multiliterate repertoires and by implementing meaningful learning contexts ( Armand, Lê et al., 2011 ). In this study, which was carried out in 12 secondary school classes for newcomers in Quebec (Canada), we assessed the effects of an intervention based on these foundations and aimed at the production of plurilingual identity texts ( Cummins et Early, 2011 ), supported by plurilingual drama workshops ( Équipes ÉRIT et ÉLODiL, 2013 ). The effects of this intervention on students’ engagement in writing were documented through participant observations and individual interviews with a sub-sample of 48 students. Results show that the production of plurilingual identity texts, especially when supported by plurilingual drama workshops, contributes more to students’ engagement in writing than traditional teaching practices used in a control group, both on an affective and cognitive level.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".