‘Good Intentions’ that ‘Do Harm’: Canada's state multiculturalism policy in the case of Black Canadians
Bibliographic record
Abstract
This essay identifies Canada's recognition of the United Nations Declaration for People of African Descent (UNDPAD) as a multiculturalist iteration. In this scope, the essay discusses the Community Support, Multiculturalism and Anti-Racism Initiatives (CSMARI) program as a central element of state multiculturalism, through which Canada plans to meet commitments to Black Canadians-and by extension, the UNDPAD. Although the CSMARI program is well intended, it causes harm to Black Canadians by reinscribing stereotyped material lack and other forms of racialized scarcity. Rather than address longstanding social-economic histories that sustain racialized poverty, state multiculturalism policy inadvertently reinforces these. The CSMARI program's focus on material lack as opposed to the systemic aspects that underpin these, amplifies Canada's multicultural myth of inclusivity while leaving unquestioned the cultural barriers that block Black citizens. State multiculturalism policy maintains the status quo by commodifying and depoliticizing anti-racism, while also neutralizing the language of naming experiences of exclusion. This essay adapts an anti-Black racism feminist theory to recast state multiculturalism as, implicitly, a cause of harm. The paper questions 'good intentions' that 'do harm' as a critical reflection that speaks to the dissonance expressed by Black Canadians, despite state multiculturalism policy.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.089 | 0.046 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".