Linguistic hierarchisation in education policy development: Ontario’s Heritage Languages Program
Bibliographic record
Abstract
Building on the recent studies revealing that official bilingualism policies in Canada are often used to reinforce a specific racial and linguistic order, this paper addresses the impact of these federal-level policies on education policies at the provincial level. From the policy genealogy perspective, we examine Ontario’s Heritage Languages Program (HLP), a highly contentious provincial policy that is still in place today. By analysing the discourses circulating in public and within the Ontario Ministry of Education around a proposed bill in the legislature to bolster heritage-language instruction and a subsequent Ministry initiative, we argue that official bilingualism and policies of multiculturalism functioned as discursive vehicles for resisting an enhanced HLP and to heritage-language education per se in politically more tolerable ways. The first part of the paper describes the research design, and introduces the historical context which produced the HLP and the early conflicts over it. The second part discusses three specific findings: (1) a discussion of the Proposal and its relationship to Bill 80; and (2) the discourses present in the general public; and (3) the discourses present in Ministry-internal deliberations of the HLP in Ontario.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".