Encoding Plasticity: The Rise of Molecular Biopolitics and Human Capital
Bibliographic record
Abstract
This article explores the fundamental concept of plasticity in living organisms and its complex relationship with capitalism and its datafication of life. Plasticity is presented as a crucial adaptive mechanism allowing organisms to respond to environmental changes. I examine how capitalism has uniquely developed methods to exploit this biological plasticity for economic growth, transforming adaptive processes into commodifiable resources, and the paradoxical nature of this relationship, where capitalist systems simultaneously depend on, and potentially undermine, the adaptive capacities of living systems. My focus is on how this exploitation ranges from genetic modification of crops to the manipulation of consumer behaviour through neuroplasticity-based marketing strategies. Furthermore, my discussion traces the historical roots of this dynamic, referencing Adam Smith’s and Karl Marx’s observations on the mechanization of labour and its connection to the division of tasks. It then expands on this by introducing Michel Foucault’s concept of disciplinary power and its role in shaping and restricting the plasticity of life within industrialized societies. The article details how this form of power operates through surveillance, normalization, and the meticulous control of bodies, as exemplified by Taylorism in factory settings. My argument goes on to explore the transition from disciplinary power to biopower, a more expansive form of control that regulates life processes on a population scale. It also recounts Foucault’s analysis of neoliberalism as a pervasive form of governmentality that shapes human conduct and subjectivity in alignment with market-oriented principles. In conclusion, this work provides an analysis of the connections between biological adaptability, economic systems, and power mechanisms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".