Synthèse critique des connaissances sur l'écriture électronique à l'aide du blogue au primaire et au secondaire
Bibliographic record
Abstract
This article offers a critical synthesis of the knowledge obtained in the wake of the work of the Comite d’experts sur l’apprentissage de l’écriture (Committee of experts on learning to write), which published a report entitled, « Mieux soutenir le développement de la compétence à écrire » (Better Support for the Development of Writing Skills) in January 2008. The synthesis focused on scientific texts that deal with the practice of electronic blog writing in elementary and high school classrooms. The period covered extends from January 2004 to May 2010. Results indicate that a blog’s contexts of use can favour students’ motivation to write, add authenticity to the writing process, and lead students to produce numerous texts. The qualitative aspect of these texts, however, has been little discussed until now. In light of the compiled texts, conceptual and methodological considerations are also proposed as potential avenues for future research.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".