Beyond the Frame of Heritage Languages: Recovering Policy Genealogies from the Historical Challenges in the Toronto Board of Education
Bibliographic record
Abstract
In Canada, heritage languages generally indicate “languages other than English and French” within the framework of official bilingualism and multiculturalism (Haque, 2012). Based on a critique of binary thinking behind the term, this study uncovers historical struggles around regulating heritage language education in the Toronto Board of Education from the mid-1970s to the early 1980s. The study draws on data from archive documents from the Board, Ontario Ministry of Education, and other database of historical newspapers. Starting in the 1970s, the Board frequently debated proposals to integrate heritage language learning into elementary schools, even after the Ontario Ministry of Education’s establishment of the Heritage Languages Program in 1977. Adopting governmentality (Foucault, 1991) as a theoretical framework and genealogy as a method (Walters, 2012), I examined how policy actors reconstructed the meanings of heritage languages and heritage language education and used discursive strategies to reform institutional policies. The analysis revealed that “heritage languages” invoked more than linguistic questions. Instead, policy actors’ ethnolinguistic and racial discourses functioned to define heritage languages and identify the need for heritage language education. Consequently, these discourses regulated various historical arguments for reframing heritage languages themselves in the context of school education. The results of this study challenge binary discourses, which help support a better understanding of heritage language and heritage language education policies and prevent reproducing similar struggles in the future.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.029 | 0.035 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".