Pharmaceuticals in the Capitalocene: on the metabolic exploitation of life and bodies in the capitalist world-ecology
Bibliographic record
Abstract
Drawing on research in environmental humanities and critical agrarian studies, this article seeks to explore the role that drugs play in the world-ecology of capitalism. We propose a “political ecology of pharmaceuticals” by approaching their transformational effects on bodies and lives – through their very capacity to reshape the metabolism of both human and non-human life. Industrial pharmaceuticals, we argue, actively participate in the capitalist process of appropriation, stimulation, and adaptation of the very fabric of life. They partake in a metabolic exploitation of life, without which the world-ecology of capitalism would not be as efficient and resilient. We highlight three dimensions of this process. The first dimension concerns the significant role pharmaceuticals played in colonization – drawing on the examples of antimalarial drugs and treatments for sleeping sickness. In the second part, we argue that the productivist model and the ongoing intensification of labor at the heart of the Capitalocene could never have been sustained at such a global scale without pharmaceuticals—as illustrated by both the industrial farming of animals and the medicalization of precarious workers in the agro-industrial world. Finally, the third dimension concerns the role pharmaceuticals play in adapting to – and sustaining – the degraded world of the Capitalocene.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".