From cryptocurrencies to cryptocourts: blockchain and the financialization of dispute resolution platforms
Bibliographic record
Abstract
This paper contributes to emerging discussions of blockchain governance through an analysis of dispute resolution platforms that reimagine justice. We focus specifically on Kleros, a blockchain-enabled dispute resolution platform, that promises to secure, authenticate, and democratize access to justice for the twenty-first century. We advance the concept of cryptocourts whereby jurors, incentivized by accumulating cryptocurrency, rapidly mobilize using principles of on-demand crowdsourcing to resolve disputes. We critique the broader social imaginaries that cryptocourts such as Kleros will result in a more open, trustworthy, transparent, and democratic systems of justice. These platforms instead pose important questions concerning their potential impact on civil dispute resolution practices by embedding it within an economy of cryptocurrency speculation. This ostensibly results in a legal infrastructure founded on principles of financial acquisition that positions jurors as economic agents seeking to profit from disputes, and courts as computational systems that merely authenticate and secure the distribution of evidence and verdicts.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".