“More languages means more lights in your house”
Bibliographic record
Abstract
Abstract French immersion programs throughout Canada have historically consisted of predominantly Anglophone populations pursuing bilingualism in the country’s two official languages, English and French. Nevertheless, recent developments in immigration and refugee resettlement have contributed to increasingly diverse student backgrounds nationwide (Statistics Canada, 2014). Researchers have explored the motivation for Allophone families to pursue FSL in Canada ( Dagenais & Berron, 2001 ; Dagenais & Jacquet, 2000 ; Mady, 2010 ); the language proficiency of Allophone learners in FSL programs ( Bérubé & Marinova-Todd, 2012 ; Carr, 2007 ; Mady, 2015 ); and the perspectives of FSL educators with respect to such learners ( Mady, 2016 ; Mady & Masson, 2018 ; Roy, 2015 ). The present study draws from interview data to explore and compare the experiences and perspectives of seven Allophone parents and 43 FI educators in Saskatchewan. In the present article, we examine the perspectives of FI educators, the experiences of Allophone families, and the implications for immersion programs worldwide.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".