Bibliographic record
Abstract
In Montreal, Quebec, Canada, legislation imposing French as the majority language of schooling has meant that since the 1970s, the French-language school system has witnessed a huge influx of ethnically and linguistically diverse students. This has posed considerable challenges for a school system that formerly received only children from French-speaking homes. Kindergartens in particular have an important language socializing function in multiethnic Montreal neighborhoods, as the children often come from nonWestern homes. Such children are introduced to both the language and the culture of the North American/French Quebec school system in kindergarten. Their future school performance depends on their being able to integrate successfully. This chapter consolidates research findings from a 5-year research project recently concluded in a Montreal “welcome class” kindergarten. Topics discussed include aspects of the children’s French conversational fluency as it develops over the school year; the way in which the teacher structures classroom interaction to promote second language acquisition in this heterogeneous environment where she is the only speaker of the target language; and the role played by the children’s home languages and cultures. The teacher developed ways to encourage children to draw on their home languages and cultures to jointly construct an L2 community that reflects a new, emerging, hybrid Quebec culture. The children were 5 years old and came from a wide variety of L1 backgrounds, including Albanian, Arabic, Bosnian, Bulgarian, Chinese, Croatian, English, Farsi, Gujrati, Haitian Creole, Pashto, Punjabi, Russian, Sinhalese, Spanish, Tagalog, Tamil, Turkish, Urdu, and Vietnamese. The children had no previous experience of schooling and spoke no French.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".