Pédagogie inversée et inclusion: Le français de scolarisation en classe d'accueil à Montréal
Bibliographic record
Abstract
An immigrants’ land, Quebec opened its first welcoming classes in the 1960s. Today, these classes reflect the multicultural, multilingual population of Quebec. They are described as closed, since they are made up exclusively of allophone students whose first language is neither French nor English, Canada's two official languages. During the 2022-23 school year, I taught in a multi-grade class, grades 4, 5 and 6, in a Montreal elementary school. Opening with just three students, we ended the year with 13. How do we combat this defection while continuing to help older students progress and train new ones to learn French? French, the language for which Quebec continually fights to the point of legislating and obliging its future citizens to speak it, by learning it from school onwards. This French is a second language, but it is above all a language of schooling. The solution seemed obvious to me: integrate the flipped classroom for children of the 21st century. This master thesis therefore describes the implementation of the flipped classroom through project-based activity, not only for learning French, but also for integration, inclusion, autonomy and putting students into action in a situation they know and experience. I had to teach them about the school and their new cultural environment and pull them all along with me.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".