Creating Canada: Education for Inclusion or Different Versions of Colonial Stories?
Bibliographic record
Abstract
In 2006, Dragon Hill Publishing released a book (the first of the series) titled How the Scots Created Canada. Since then, it has released six additional books detailing how the groups they identify as Italian, French, English, Black, Chinese, and Polish have each “created” Canada. Dragon Hill claims that their aim “is to produce and market books to the popular adult and youth markets … that will help individuals improve their self-image and that will help increase understanding and tolerance among diverse individuals and cultures.” In this article we examine how, rooted in the Canadian multicultural discourse, these “count me in” narratives of the featured ethnically and racially minoritized Canadians are presented as “add-ons” rather than integrated into the “Canadian narrative.” We explore how attempts at inclusive education and the quest to dispel, for some ethnic groups, the perpetual foreigner trope re-inscribe an uncritical embrace of Western European narratives based on discourses of whiteness, individualism, and conquest. Employing critical theories relating to decolonization, we highlight how the “counter-narratives” presented in the series serve to accommodate and simultaneously become complicit in “creating” problematic, incomplete, contradictory, and misrepresentative narratives of Canada and the featured minoritized groups.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.049 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".