Interracial Unions with White Partners and Racial Profiling: Experiences and perspectives
Bibliographic record
Abstract
Over the past decade racial profiling has received much scholarly and public attention. Our study explores the awareness, perspectives and experiences of the individuals in interracial unions with White partners. We found most White partners ’ awareness and objection to racial profiling arose from vicarious experiences with racialized partners who are subjected to everyday racism including racial profiling. White women, in general, exhibited a fairly high degree of anxiety about their partners being racially profiled. Women ‘of colour ’ exhibited varied levels of awareness and experience with racial profiling. Most men ‘of colour ’ in our study experienced racial profiling, but two provisionally accommodated themselves to the practice. Our study indicates few couples felt they were racially profiled because of their mixed union though couples with young Black men and White women were the exception. All couples experienced overt and covert forms of discrimination and some felt their hypervisibility as interracial couples opened them to consistent regulatory surveillance. We describe the latter as a process of ‘repressive tolerance ’ and offer thoughts on future study. This research suggests racial profiling and repressive tolerance have points of convergence in how interracial couples make sense of law enforcement and their place in Canadian society.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".