The Realities of Racism: Exploring Attitudes in Manitoba, Canada
Bibliographic record
Abstract
Between December 2020 and January 2021, we conducted an online mixed-methods survey to explore racism in the province of Manitoba, Canada. The survey was completed by exactly 500 residents of the province and was largely representative of the demographics of the province. The survey measured views on racism, multiculturalism, religious diversity, assimilation and linguistic diversity, and also explored lived experiences with racism. In this article, we report respondents’ views on multiculturalism, religious diversity, assimilation and racism. The strong majority of Manitobans recognized that racism is a problem in their area of the province, and yet views towards assimilation and support for religious diversity remain mixed. These findings show contradictions between overall support for broad themes like diversity or multiculturalism yet high levels of continuing discrimination and racism in the province. Our findings emphasize the impacts of whiteness, with the intersectional complexities further emphasized by the qualitative stories shared by participants, giving accounts of racism at work, in stores, healthcare, justice and in different demographic groups. Specifically, incidents of racism against Indigenous Peoples were the most commonly experienced and witnessed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".