The Homogenization of Citizenship in the Ontario Curriculum: A Historical Analysis
Bibliographic record
Abstract
While many Canadians take pride in living in an inclusive and multicultural society, various researchers have suggested that Canadian multiculturalism is just a new approach to assimilating minorities into the dominant culture. In this regard, recent analyses of citizenship education curricula in Ontario and other Canadian provinces have suggested that these curricula present a homogenous society and a universal, neutral, and de-contextualized model of citizenship. As citizenship is re-imagined to be generic and individual, the multilayered communities of Canada are glossed over, and replaced with a single, undifferentiated, imagined community. So far, however, there has been very little analysis of how these homogenizing patterns in the Ontario curriculum developed over recent decades. Through a comparative analysis of the 1999, 2005, and 2013 revisions of Ontario citizenship curriculum documents, this paper offers insight into these historical developments. The Ontario curriculum is shown to have gradually de-emphasized local and cultural expressions of citizenship in favour of global and economic expressions of citizenship. Such developments align with much recent research on the neoliberalization of education policy. In conclusion, this paper addresses the specific implications of these developments for citizenship education in urban schools with diverse and marginalized student populations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".