Technoscience Rent: Toward a Theory of <i>Rentiership</i> for Technoscientific Capitalism
Bibliographic record
Abstract
Contemporary, technoscientific capitalism is characterized by the (re)configuration of a range of “things” (e.g., infrastructure, data, knowledge, bodies) as assets or capitalized property. Accumulation strategies have changed as a result of this assetization process. Rather than entrepreneurial strategies based on commodity production, technoscientific capitalism is increasingly underpinned by rentiership or the appropriation of value through ownership and control rights (e.g., intellectual property [IP]), monopoly conditions, and regulatory or market devices and practices (e.g., investment dispute courts, exclusivity agreements). While rentiership is often presented as a negative phenomenon (e.g., distorting markets, unearned income) in both neoclassical and Marxist political economy literatures—and much in between—in this paper, I conceptualize rentiership as a technoeconomic practice and process framed by insights from science and technology studies (STS). So, rather than a problematic “side effect” of capitalism, the concept of rentiership enables us to understand how different forms of value extraction constitute, and are constituted by, different forms of technoscience. This allows STS to contribute a distinctive analytical approach to ongoing debates in political economy about economic rents and rent-seeking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".