Health prevention in the era of biosocieties: a critical analysis of the ‘Seek‐and‐Treat’ paradigm in<scp>HIV</scp>/<scp>AIDS</scp>prevention
Bibliographic record
Abstract
On 18 November 2014, the United Nations launched an urgent new campaign to end AIDS as a global health threat by 2030. With its proposed strategy, the UN follows leading scientists who had declared the failure of former prevention strategies and now were promoting a 'Seek and Treat for Optimal Prevention' (STOP) approach as the most cost-effective response to the pandemic to meet the goal of 'an AIDS-free generation'. STOP combines antiretroviral therapy and routine HIV screening to find persons unaware that they are HIV-positive, because research has shown that people consistently change their behaviour (i.e. increase condom use, have fewer partners) after an HIV diagnosis. AIDS activists have broadly criticized this strategy on different levels. In this article, we go beyond these criticisms and try to analyse the political rationalities behind this 'new' strategy. We believe that it is necessary to put the rationale underpinning the STOP programme into the context of broader societal transformations that can best be captured as the development of advanced liberal societies and the new emphasis on self-controlling or self-responsibility rather than on disciplining behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.017 | 0.147 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.017 | 0.032 |
| 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".