Screening out Diseased Bodies: Immigration, Mandatory HIV Testing, and the Making of a Healthy Canada
Bibliographic record
Abstract
From the late nineteenth century onwards, health has been a technology of governance constitutive of national borders and racial boundaries. As many scholars have documented in various geographical contexts, nineteenth and twentieth-century public health policies have been intricately linked to racialized nation-formation in several ways. Whereas disease and ill-health were often the racial mark of the ‘colonized’ and ‘uncivilized’, the racialized concept of (European) citizenship was historically imagined through ideas around health and vitality. 1 Today, as we move into the twenty-first century, public health remains an imperative of nation-formation. If contagion was historically seen as ‘the dark side of the civilizing mission’ as Michael Hardt and Antonio Negri claim, in the twenty-first century contagion remains a constant and present danger, but is now the dark side of globalization. 2 Global flows of knowledge, capital, migrant labor, and travel — and the rapid speed at which these now occur — have opened up even greater possibilities for the transmission of germs and disease. ‘If we break down global boundaries and open up universal contact in our global village’, ask Hardt and Negri ‘how will we prevent the spread of disease and corruption?’ 3 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".