Multiculturalism and human rights in civic education: the case of British Columbia, Canada
Bibliographic record
Abstract
Background: This paper considers how textbooks resolve the tension between contradictory goals of promoting a cohesive national identity while teaching respect and equality among diverse social groups in British Columbia (B.C.), Canada. Purpose: The article presents preliminary results of a larger study examining the content of required civic education textbooks in Canada to examine whether and how notions of national identity incorporate the principles of human rights and multiculturalism. Sample: The study draws on curricular material for required high school social science courses in B.C. The sample includes textbooks starting the first year of high school (Grade 8) and covers each year through high school graduation (Grade 12). The central analyses examine the content of 17 core textbooks approved by the provincial government for these courses. Design and methods: This research systematically examines the content of currently approved textbooks for high school social science courses in B.C. Aquestionnaire designed using the principles of content analysis measures textbook emphases on content relevant to human rights, multiculturalism and national identity. Results: This study finds that traditional notions of national identity are reshaped in response to the rise of emphases on human rights and multiculturalism. Rather than depicting national identity as stemming from a common race, ethnicity, language or history, the government pursues four main strategies to simultaneously promote human rights, multiculturalism and a shared national identity: (1) framing human rights and multiculturalism as part of national identity; (2) using pedagogical approaches that promote multiple perspectives; (3)celebrating social and scientific figures and accomplishments as a main source of national pride; and (4) drawing on exogenous sources to affirm state legitimacy. Conclusion: In a context that values diversity and human rights, contemporary sources of national identity can stem from facets of society that can transcend many cultures and emphasise organisational aspects of the nation-state. A main implication is that the inclusion of principles of human rights and multiculturalism into civic education is changing traditional conceptions of national identity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".