Ruling through technology: politicizing blockchain services
Bibliographic record
Abstract
Next to artificial intelligence and big data, blockchains have emerged as one of the most oft-cited technologies associated with the digital economy. Leading technology companies have recently contributed to making the technology used more widely by developing integrated blockchain offerings. The emergence of such services yet strikingly clashes with the original stated goal of the technology to remove any form of central political authority, such as the one companies behind these new services can represent. How should we then understand the embrace of blockchains by companies that this technology was notably supposed to displace? Using the concept of infrastructure from Science and Technology Studies, we argue that these companies are not merely adopting the technology but actively promoting a new assemblage of socio-technical devices to reassert their authority over how information is exchanged online. Based on a comparative analysis of the technical documentation of Ethereum and Amazon Web Services (AWS) blockchain services, we highlight how actors contributing to building digital infrastructures regulate their users' behavior by affording them different capacities and constraints. We moreover show how by pursuing its commercial interest, AWS supported a corporate form of governance historically promoted by the United States to oversee the digital economy.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".