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
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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How this classification was reachedexpand
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 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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".