Heterogeneous practices and homogenizing policies: Towards a typological analysis of Canadian citizenship education in Ontario
Bibliographic record
Abstract
Abstract In 2016, rumours began to spread that the Ontario Ministry of Education was quietly considering cutting its mandatory high school civics course in order to expand another mandatory course on career preparation. While the Ontario Ministry of Education ultimately backed down from this controversial position, the resulting public dialogue raised important questions regarding what Ontario hopes to accomplish through citizenship education. In this article, I engage this debate primarily from a theoretical perspective, arguing that the ambiguous role of citizenship education stems in a large part from a lack of clarity regarding how we use the term ‘citizenship’. In the first and second section, I review the existing literature, with particular emphasis on educational scholarship and political philosophy, to illustrate the incongruity between the complex and multi-layered ways in which citizenship is enacted in Canada and the superficial and homogeneous manner in which it is portrayed in Ontario curricula and educational policies. This incongruity, I suggest, illustrates the need for greater analytic clarity regarding the meaning(s) of citizenship. In the following section, I propose a typology of citizenship, featuring five dimensions along which citizenship is primarily enacted – political, legal, public, economic and cultural. Finally, I illustrate this typology through a brief empirical analysis of the Ontario civics curriculum – a comparative keyword content analysis of the 1999, 2005 and 2013 versions of the curriculum policy document. In conclusion, I suggest that there has been a historical shift in Ontario educational policy towards economic expressions of citizenship at the expense of other dimensions. In this sense, the brief controversy over cutting the Ontario civics course to expand the careers curriculum can be seen as just one manifestation of a larger policy trajectory.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.035 |
| Science and technology studies | 0.027 | 0.031 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 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".