Beyond multiculturalism: revisioning a model of pandemic anti-racism education in post-Covid-19 Canada
Bibliographic record
Abstract
Abstract Canada was the first country in the world to establish multiculturalism as its official policy for the governance of diversity. Canadian multiculturalism has gained much popularity in political and public discourses in the past 50 years, and it has also received no less criticism as to its effectiveness in addressing issues of racism. There have also been ambiguities over the meaning and intention of multiculturalism, leading to divergent understandings of multiculturalism as an ideal of inclusion and equity, on the one hand, and a mere political rhetoric, on the other. On the occasion of celebrating the 50th anniversary of Canada’s official multiculturalism policy, this article re-visits Canada’s multiculturalism by reviewing its history and ethos and critically examining its actual effects as manifested during the Covid-19 pandemic in Canada. The rise of anti-Asian racism, anti-Black racism, and anti-Indigenous racism incidents in the pandemic reveals that multiculturalism has in effect, sustained a racist and unequal society of Canada with racism entrenched in its history and ingrained in every aspect of its social structure. Multiculturalism tolerates cultural difference but does not challenge an unjust society premised on white supremacy. The anti-racism movement mobilized by racialized communities in Canada indicates that multiculturalism has failed to respond to racialized communities’ pressing demand for social change and action for social justice. The article concludes with a proposed alternative framework to multiculturalism, that is, pandemic anti-racism education model, to centre the issue of race and racism in an action-oriented, inclusive, and empowering approach toward a future of a just society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.035 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".