Bibliographic record
Abstract
Intellectual property has traditionally concerned itself with the ownership of inventions and expressions of ideas. Although the digitization of assets did create new challenges for enforcement, it did not fundamentally alter the nature of the property itself; if one traded in digital assets, one was simply trading in mere copies. However, the playing field was altered by the introduction of cryptocurrencies, which — for the first time — allow for actual transfer of digital assets. Legal scholars have already identified that cryptocurrencies (i.e., alternative currencies powered by blockchain technology, the most popular of which is Bitcoin) pose thorny issues for property law. In this paper, I offer a conceptual framework to understand the blockchain as a departure from traditional intellectual property law. I review foundational ideas about intellectual property theory (Locke’s labour theory, Hegel’s personalty theory, Bentham’s utilitarianism, the modern common law legal fiction of the “bundle of sticks”, as derived from Hohfeld) and demonstrate that none of these philosophical bases are sufficient for a technology as revolutionary as the blockchain. Instead, I propose an alternative property conception, which invokes civilian masters Savigny and Duguit, and in particular, their views regarding patrimony.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".