‘Race’ and HIV vulnerability in a transnational context: the case of Chinese immigrants to Canada
Bibliographic record
Abstract
Although immigrants' sustained connections with their homelands are well documented, so far we know little about how 'race' - in particular, conceptions of race back home - influences the HIV vulnerability of racialised immigrants to Western countries. Drawing on data from a multi-sited, qualitative study of Chinese immigrants to Canada, this paper presents a contextualised understanding of the impacts of race on HIV risk faced by these individuals in a transnational context. Data were collected from four study sites in Canada and China as part of a study investigating the relationship between HIV risk and transnationalism. Although race appears to have bearing on their risk perceptions and sexual practices, immigrants' understandings of race are not necessarily consistent with dominant discourses of race in Canada, but are also mediated by their racial habitus developed in China. Findings reveal the complex power dynamics - not just power asymmetries but also power fluidity - around race from a transnational perspective and thus challenge the assumed dichotomy of dominance and subordination underpinning traditional explanations of the relationship between race and HIV risk. In the context of transnationalism, researchers should go beyond a nation-bound concept of society (i.e. the host society) and take into account the simultaneous influence of both host and home countries on immigrant health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".