Ambivalence and the biopolitics of HIV pre-exposure prophylaxis (PrEP) implementation
Bibliographic record
Abstract
Ambivalence, the vacillation between conflicting feelings and thoughts, is a key characteristic of scientific knowledge production and emergent biomedical technology. Drawing from sociological theory on ambivalence, we have examined three areas of debate surrounding the early implementation of HIV pre-exposure prophylaxis, or PrEP, for gay, bisexual, queer, and other men who have sex with men in Canada, including epistemology and praxis, clinical and epidemiological implications, and sexual politics. These debates are not focused on the science or efficacy of PrEP to prevent HIV, but rather represent contradictory feelings and opinions about the biopolitics of PrEP and health inequities. Emphasizing how scientists and health practitioners may feel conflicted about the biopolitics of novel biomedical technologies opens up opportunities to consider how a scientific field is or is not adequately advancing issues of equity. Scientists ignoring their ambivalence over the state of their research field may be deemed necessary to achieve a specific implementation goal, but this emotion management work can lead to alienation. We argue that recognizing the emotional dimensions of doing HIV research is not a distraction from "real" science, but can instead be a reflexive site to develop pertinent lines of inquiry better suited at addressing health inequities.
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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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".