Promoting Ethnocultural Equity Education in Franco-Ontarian Schools
Bibliographic record
Abstract
The Franco-Ontarian community has always been made up of people whose language and culture were similar. Over the past 20 years, important changes have taken place, particularly in urban centres where the French-speaking immigrants came from all parts of the world. A veritable micro cosmos has resulted, reflecting the growth of the world community of francophones (Ministry of Education and Training, 1993). Ontario is a large province with regional differences which are reflected in the concentration and composition of the population. In a number of towns, French is the language of the majority but francophones can be found across the province. A large number of newly arrived immigrants from other French-speaking environments, have settled in Ontario, especially in urban regions, where they are now actively engaged in maintaining and promoting the use of French within their respective ethnocultural communities. Given the historical context of the evolution of French schools in Ontario, the arrival of new francophones can be beneficial. This is the basis from which one must examine the position of French-language schools in Ontario when redefining their role and the changes that must be made to include these new groups of francophones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".