Mapping HIV community viral load: space, power and the government of bodies
Bibliographic record
Abstract
HIV plasma viral load testing has become more than just a clinical tool to monitor treatment response at the individual level. Increasingly, individual HIV plasma viral load testing is being reported to public health agencies and is used to inform epidemiological surveillance and monitor the presence of the virus collectively using techniques to measure 'community viral load'. This article seeks to formulate a critique and propose a novel way of theorizing community viral load. Based on the salient work of Michel Foucault, especially the governmentality literature, this article critically examines the use of community viral load as a new strategy of government. Drawing also on the work of Miller and Rose, this article explores the deployment of 'community' through the re-configuration of space, the problematization of viral concentrations in specific microlocales, and the government (in the Foucauldian sense) of specific bodies which are seen as 'risky', dangerous and therefore, in need of attention. It also examines community viral load as a necessary precondition - forming the 'conditions of possibility' - for the recent shift to high impact prevention tactics that are being scaled up across North America.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".