Transition Strategies for Newcomers to the French School Milieu in Ontario
Bibliographic record
Abstract
This is a follow-up on a previous study on school age French first language newcomers to English speaking Ontario that showed attitudes and concerns around welcoming strategies and integration policies put in place at the Provincial level. New paths were delineated as a result of recommendations made. The findings led to the formulation of policy recommendations as well as follow-up guidelines for the French school system in Ontario, keeping in mind newcomers' needs as well as looking at the local community where these persons were interacting, namely as regards welcoming mechanisms and strategies to facilitate integration into the community. Since then, a number of implementations have been made in order to follow-up on initial observations regarding points such as the need to take into account attitudes towards diversity and the need of a collective effort. In this paper we discuss the study results, compare the ways implementations were made as well as report on the present situation through the analysis of several recent Ministry documents. The most interesting aspect emerging from the process appears to be the consistency in which consecutive Ministry documents address the relevant issues with a progressive zeroing in on detail as if using a zoom lens with the intention for objectives to be met through progressive adjustments. Overall, the diverse documents published, including the recently recommended implementation guidelines to 'support each pupil' appear to satisfy most concerns expressed in the initial study.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".