Bibliographic record
Abstract
In recent years, HIV/AIDS programming has been transformed by an ostensibly 'new' procedure: male circumcision. This article examines the rise of male circumcision as the 'right' HIV prevention tool. Treating this controversial topic as a 'matter of concern' rather than a 'matter of fact', I examine the reasons why male circumcision came to be seen as a partial solution to the problem of HIV transmission in the twenty-first century and to what effect. Grounded in a close reading of the primary literature, I suggest that the embrace of male circumcision in HIV prevention must be understood in relation to three factors: (1) the rise of evidence-based medicine as the dominant paradigm for conceptualising medical knowledge, (2) the fraught politics of HIV/AIDS research and funding, which made the possibility of a biomedical intervention attractive and (3) underlying assumptions about the nature of African 'culture' and 'sexuality'. I conclude by stressing the need to expand the parameters of the debate beyond the current polarised landscape, which presents us with a problematic either/or scenario regarding the efficacy of male circumcision.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".