The problem <i>of</i> innovation in technoscientific capitalism: data <i>rentiership</i> and the policy implications of turning personal digital data into a private asset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A spate of recent scandals concerning personal digital data illustrates the extent to which innovation and finance are thoroughly entangled with one another. The innovation-finance nexus is an example of an emerging dynamic in technoscientific capitalism in which innovation is increasingly driven by the pursuit of “economic rents”. Unlike innovation that delivers new products, services, and markets, innovation as rentiership is defined by the extraction and capture of value through different modes of ownership and control over resources and assets. This shift towards rentiership is evident in the transformation of personal digital data into a private asset. In light of this assetization, it is necessary to unpack how innovation itself might be a problem, rather than a solution to a range of global challenges. Our aim in this paper is to conceptualize this relationship between innovation, finance, and data rentiership, and examine the policy implications of this pursuit of economic rents as a deliberate research and innovation strategy in data-driven technology sectors.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it