Growing up African Canadian in Vancouver: Racialization, Gender and Sexuality
Bibliographic record
Abstract
Vancouver is one of the most diverse cities in North America, with 49% of the population identifying as people of colour. However, residents who are racialized as Black or claim an African ethnic origin make up just over 1% of the population. These residents may constitute a hyper-visible minority in the local context, but they are firmly embedded in discourses about Blackness that transcend local geographies. Based on interviews with 35 adult children of immigrants from sub-Saharan Africa, this paper explores some of the ways that gendered and sexualized discourses of Blackness shape the lives of men and women in metro Vancouver. Interactions in public spaces include challenges to competency, honesty, and respectability, while private lives are marked by differences in heterosexual desirability that enhance the romantic prospects of men and limit those of women. The following discussion illustrates that processes of racialization are simultaneously gendered and sexualized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".