Contradictory Mobilities and Cultural Projects of Afropolitanism African Immigrant Nurses in Vancouver, Canada
Bibliographic record
Abstract
I explore the relationship between social class and race, through an examination of how Black nurses enact Afropolitan cultural practices to negotiate contradictory class mobilities in Vancouver. While this paper reflexively draws from my family’s lived experiences to begin thinking through the nuances of Afropolitanism, I hone the discussion in contextual reference to the class-making practices of African-born nurses. The nurses channel Afropolitan class-making projects, through which they develop a flexibility and openness of mind that enables them to reject taking on the role of victim in their contradictory mobilities. Afropolitanism refers to “an expansive politics of inclusion that seeks to position actors as part of a transnational community of Africans of the world” (Adjepong 2021, 1), to “imbue Africanness with value” (137). Merging the literature on anti-Black racism in nursing with scholarship examining relationships between social class, race, and culture, this paper draws out the promises and pitfalls of Afropolitanism through an exploration of how African immigrant nurses—part of a growing Black Canadian middle class—grapple with contradictory mobility in Canada’s racialized terrain. It contributes to discussions of the Black middle class, in the context of a “relative newness of Black middle classes” (Rollock et al. 2012, 253).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.057 | 0.022 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".