Citizenship education in Canada: ‘Democratic’ engagement with differences, conflicts and equity issues?
Bibliographic record
Abstract
Abstract Recent research on multi-faceted citizenship education policy and practice in Canada illustrates five enduring themes of interest to educators around the world. First, citizenship education policy mandates reveal diverse goals for ‘good’ or ‘active’ citizen engagement, critical and inclusive awareness, and skills. Students from different social identity and status locations tend to have unequal citizenship learning experiences, and school education is often disconnected from their lived experiences and concerns. Second, intersecting questions of national and ethno-cultural identity and social justice are prominent in Canadian curricular rhetoric, although achievement of inter-group equity, mutual understanding and justice is elusive. Third, although transnational issues and perspectives are increasingly included, some Canadian curricula seem to reinforce ignorance and stereotypes about other nations and peoples and about the causes of global problems such as war. Much of the global citizenship education activity in Canadian schools seems to be focused on co-curricular activities, often emphasizing charity fundraising, leaving the causes of human misery largely uninterrogated. Fourth, curriculum policy discourse in civics, social sciences, language and media literacy emphasizes the importance of student-centred pedagogy for development of critical thinking skills, while typical classroom practice seems often to retain teacher-centred transmission approaches. Last, implicit citizenship education is embedded in day-to-day school-related activities and relationships: patterns of discipline and conflict management, community service activities, and student voice and leadership roles. Thus active, engaged citizenship, attentive to multicultural diversity, is a prominent goal in recent Canadian citizenship education policy and programming – yet in practice, Canadian students (especially those from less privileged backgrounds) have few opportunities to practice democratically relevant citizenship learning in school.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".