Body–drug assemblages: theorizing the experience of side effects in the context of <scp>HIV</scp> treatment
Bibliographic record
Abstract
Each of the antiretroviral drugs that are currently used to stop the progression of HIV infection causes its own specific side effects. Despite the expansion, multiplication, and simplification of treatment options over the past decade, side effects continue to affect people living with HIV. Yet, we see a clear disconnect between the way side effects are normalized, routinized, and framed in clinical practice and the way they are experienced by people living with HIV. This paper builds on the premise that new approaches are needed to understand side effects in a manner that is more reflective of the subjective accounts of people living with HIV. Drawing on the work of Deleuze and Guattari, it offers an original application of the theory of 'assemblage'. This theory offers a new way of theorizing side effects, and ultimately the relationship between the body and antiretroviral drugs (as technologies). Combining theory with examples derived from empirical data, we examine the multiple ways in which the body connects not only to the drugs but also to people, things, and systems. Our objective is to illustrate how this theory dares us to think differently about side effects and allows us to originally (re)think the experience of taking antiretroviral drugs.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".