FONDEMENTS THÉORIQUES ET EMPIRIQUES DE L'ARTICULATION LECTURE-ÉCRITURE EN LANGUE MATERNELLE ET EN LANGUE SECONDE
Bibliographic record
Abstract
The question of relations between reading and writing arises increasingly among didactic researchers. To better understand this issue and to shed light on the connections between these two linguistic practices, many studies are conducted. The consideration of these relations could facilitate the implementation of educational programs and improve students’ production and comprehension in mother tongue (L1) and in a second language (L2). The objective of this study is to review various concepts related to the reading-writing connections. First, we take a historical and cultural look at reading and writing relations knowledge. Then, the connections between these two linguistic practices are studied through several educational theories in L1 and in L2. We focus, afterwards, on the profits and on the need to combine reading and writing activities. We study also how the knowledge acquired in one of these fields can be transferred to the other and what are the factors that determine this transfer process. Some empirical studies are also analyzed. Finally, students' conceptions about the relations between reading and writing and the principles of the readingwriting combination activities are presented.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 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".