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Record W4417166000 · doi:10.1080/03066150.2025.2584783

Pharmaceuticals in the Capitalocene: on the metabolic exploitation of life and bodies in the capitalist world-ecology

2025· article· en· W4417166000 on OpenAlexaff
Pierre‐Marie David, Nicolas Le Dévédec

Bibliographic record

VenueThe Journal of Peasant Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsHEC MontréalUniversité de Montréal
Fundersnot available
KeywordsProduction (economics)CapitalismConsumption (sociology)Agriculture

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.307
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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