Official language minorities and geographic perspectives: Ontario's dual approach to curriculum development
Bibliographic record
Abstract
Abstract Since the enactment of the Canadian Charter of Rights in 1982, the federal government has recognized Canadian citizens' right to have their children educated in their first official language (French or English), including in settings where a minority of the population speaks that language. As a case in point, Francophones (a term commonly used to describe people who speak French as their first language, and/or speak French in the home) have gained a degree of autonomy in developing curriculum for its French‐language schools in Ontario, a majority English‐speaking province. Ontario has two distinct versions of curriculum: an English‐language version (ELV) for use in English‐language schools and a French‐language version (FLV) for use in French‐language schools. Drawing on a qualitative content analysis, this paper explores the two language versions of the curriculum through the lens of the grade 9 Issues in Canadian Geography course. The study of geography examines the nature of patterns and connections between different places and peoples, and the ways places are transformed through cultural and linguistic processes. The analysis provides insights into the ways and the extent to which Ontario's dual approach to curriculum development supports the socio‐spatial and spatial‐linguistic perspectives and experiences of Francophones as the province's official language minority population. The dual approach is theorized as a curricular core‐periphery dynamic, whereby elements of the French‐language version are peripheralized in relation to the perspectives and priorities of an English‐language dominant core.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.025 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".