In-Group Bias and Inter-Group Dialogue in Canadian Multiculturalism
Bibliographic record
Abstract
African-Canadians continue to bear the brunt of marginality and stereotyping in Canada even when various mitigating studies and programs have been initiated by the government at federal, state, and municipal levels. These stereotypes continue to affect them in informal settings and state institutions when seeking employment, housing or when in the streets, malls, schools, etc. While social justice advocates, social workers, and policy-makers focus on “Black-White” dynamics because other “racialized minorities” are also marginalized (though not equally) in Canada, it is important to note that “non-White” Canadians also contribute to the spread of historical stereotypes of African-Canadians within Canadian multiculturalism as noted in the emphasis of the city of Toronto’s mitigating strategies for “anti-Black racism.” Using social group position theory (SGPT) and asset-based model (ABCD), this paper argues that interrogating social group biases beyond “Black-White” binarism to encourage inter-group dialogs is important in making sure that different multicultural communities understand one another through favorable, activities-mediated, inter-group relations as opposed to having multicultural relations mediated by third parties, or not mediated at all. We also argue that African-Canadians should focus on internal strengths and only use external help to augment community initiatives to change the extant negative image.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.024 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".