Related and Conflated: A Theoretical and Discursive Framing of Multiculturalism and Global Citizenship Education in the Canadian Context
Bibliographic record
Abstract
There is a public perception that Canada is an ideal place for cultivating global citizenship because of its culturally plural demographics and official policies of multiculturalism. Global Citizenship Education (GCE) is a growing field in Canadian education and is an explicit focus in the Alberta social studies curriculum. This thesis brings together four conversations within which multiculturalism and GCE are both related and conflated: (a) the public perceptions of Canada as a model of cultural diversity and global citizenship, (b) the scholarly discussions of GCE and multiculturalism, (c) the policy context where multiculturalism is set alongside GCE, and (d) the practical ways that the two are mutually related in curriculum and lesson documents. There are four interrelated sections to this thesis; each identifies the tensions inherent to multiculturalism, GCE, and the perceived relationship between these fields. First is a wider philosophical and theoretical framing of the topic. Second is the examination of educational research on the topic. Third is a critical discourse analysis of policy, curriculum, and lesson plan documents in the province of Alberta. Last is a synthesis of the findings from all three sections. \nThe analysis finds that there are philosophical and ideological tensions inherent to both fields and to the relationships between them. This contributes to conceptual and ideological conflation and confusion. This finding raises some important concerns in terms of possibilities and constraints to thinking about cultural diversity and social inequities in new ways. It highlights how multicultural contexts of GCE can lead to the recreation of tensions, conflation, and ambiguity. However, the Alberta context demonstrates that a multicultural context can also open critical spaces and possibilities for GCE through engagements with tensions and complexities. Thus this thesis contributes theoretically, by presenting a framework and perspective for interrogating and critically inquiring into the relationship between the two fields. It also contributes to the policy and curriculum discussions in educational research and practice by highlighting the importance of foregrounding key tensions inherent to each field and by identifying the potential negative consequences of leaving these tensions implicit.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.043 | 0.105 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".