Targeting Fallible Men: Communication Strategies and Moral Issues in a Pre-exposure Prophylaxis Trial
Bibliographic record
Abstract
Based on the analysis of a French pre-exposure prophylaxis trial (Ipergay), and focusing on the communication strategies used to recruit volunteers, this article explores the figure who serves to justify the trial and who shapes the way in which populations concerned by this prevention tool are targeted. We show that this figure is that of the fallible man, a classic in moral philosophy: while aware of what is good or right for him, he is unable to put this knowledge into practice. The targeting of fallible men makes sense in the context of a resurgence of high-risk behaviors objectified in the late 1990s: qualifying gays who take risks as fallible individuals create a distance with respect to the "barebacker" who risks his life deliberately and has no intention of changing his behavior. Recognizing that certain gays are vulnerable to risk also provides justification for a preventive strategy that acknowledges the inadequacy of behavioral prevention, without giving up on prevention altogether. All in all, this analysis shows that the technological and epidemiological realism often highlighted in pre-exposure prophylaxis interventions is not without a moral dimension, attentive to individuals' contradictions and singularities, doubts and uncertainties, and to the risk of stigmatization inherent to the acknowledgement of risk-taking.
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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 | 0.242 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| 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".