Mobilizing the self-governance of pre-damaged bodies: neoliberal biological citizenship and HPV vaccination promotion in Canada
Bibliographic record
Abstract
Nikolas Rose and Carlos Novas use the term biological citizenship broadly to describe the increasing connectivity of biological categories of citizens' identities. In line with Rose and Novas, social scientists use biological citizenship today to describe the emergence of citizens' rights to protection and their increased mobilization around biology as a claim to active citizenship. In this article, I critically engage with the conception of biological citizenship forwarded by Rose and Novas, and detail the ways in which this concept is more complex and less emancipatory than is often assumed – especially in today's neoliberal age. Drawing on the example of human papillomavirus (HPV) vaccination promotion in Canada, I elucidate the intricacies and complex techniques that are often involved in citizenship projects. Specifically, I position HPV vaccination biocitizenship as a biopolitical tool, and pay close attention to the forms of knowledge, practical mechanisms, and types of authoritative bodies that frame biological risks for HPV and bioidentities in gendered ways. It is hoped that, through this example, the scope of biocitizenship can be expanded to encompass more than the rights and entitlements of citizens in relation to their biologies. I conclude by offering insights into theorizing emerging neoliberal biocitizenship projects today.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".