Digital Assets, Blockchains and Relativity of Title
Bibliographic record
Abstract
The Law Commission has recently published its final report on Digital Assets. 1 It is a wideranging look at the private law and characterisation of such assets.One fundamental question that must be asked about digital or crypto-assets is how we conceptualise ownership of them.The Law Commission make a number of recommendations and proposals, concerning the nature of property in digital assets and whether it can be seen as a tertium quid, traditionally seen as impossible because all assets are either choses in possession or choses in action. 2 It is also concerned with control as an indicium of legal title, transfers and collateral arrangements, and remedies for interference with such assets.3 This article concentrates on those recommendations and conclusions that bear on the nature of digital assets as private property.These questions are important; case law is reasonably clear that digital assets are potentially objects of property, 4 but doubts and questions remain.Technology alone does not provide an answer to competing claims to assets.Technology can prevent on-chain double spending, but it provides no solution to creditor claims, arising off-chain.Execution could be obtained against a defendant, but no immediate technical solution prevents the latter transferring their crypto-assets away.There is no technical solution to prevent hackers transferring crypto-assets away, appearing as the owner and passing the assets to third parties; * Professor of Business Law, University of Leeds.An early version of this paper, based largely on the Commission's consultation paper, was presented at the Obligations X conference in Banff, Canada in July 2023.My thanks to the participants in the discussion, particularly to Kelvin Low.My thanks also go to members of the City of London Law Society Financial Law Committee and Mark Evans (Travers Smith LLP) in particular for discussing these issues and their own (very similar) concerns with me.Any remaining errors are mine alone.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".