From getting along to democratic engagement: Moving toward deep diversity in citizenship education
Bibliographic record
Abstract
For much of Canada's history, diversity has been a defining characteristic of the country and has preoccupied and bedevilled policymakers. Policy and practice in Canada has moved from attempts to assimilate minority groups to fostering respect and appreciation for diversity. We argue, however, that attention to diversity education remains superficial and limited. In this article we provide an overview of policy and practice in education about and for diversity in Canada, make connections between that and policy and practice in citizenship education. We also review findings from research in the area, and lay out possible directions for moving the field forward. Like other democracies Canada has struggled to balance recognition and respect for diversity with concerns about social cohesion and we believe Canada's unique experience in this area can provide valuable insights to researchers and practitioners in other jurisdictions.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.051 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".