Discrimination Experienced by Immigrants, Racialized Individuals, and Indigenous Peoples in Small‐ and Mid‐Sized Communities in Southwestern Ontario
Bibliographic record
Abstract
We investigate discrimination experiences of (1) immigrants and racialized individuals, (2) Indigenous peoples, and (3) comparison White non-immigrants in nine regions of Southwestern Ontario containing small- and mid-sized communities. For each region, representative samples of the three groups were recruited to complete online surveys. In most regions, over 80 percent of Indigenous peoples reported experiencing discrimination in the past 3 years, and in more than half of the regions, over 60 percent of immigrants and racialized individuals did so. Indigenous peoples, immigrants and racialized individuals were most likely to experience discrimination in employment settings and in a variety of public settings, and were most likely to attribute this discrimination to racial and ethnocultural factors, and for Indigenous peoples also their Indigenous identity. Immigrants and racialized individuals who had experienced discrimination generally reported a lower sense of belonging and welcome in their communities. This association was weaker for Indigenous peoples. The findings provide new insight into discrimination experienced by Indigenous peoples, immigrants and racialized individuals in small and mid-sized Canadian communities, and are critical to creating and implementing effective anti-racism and anti-discrimination strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".