DIGITAL FINANCIAL ASSETS THROUGH THE PRISM OF THE DOCTRINE OF UNDOCUMENTED SECURITIES
Bibliographic record
Abstract
Abstract: digital reform puts on the agenda issues related to the legal regime of new digital phenomena – digital rights, including digital financial assets. Giving digital rights an independent legal status makes it necessary to differentiate the legal regime of digital financial assets and the legal regime of related objects of civil rights – non-documentary securities. The author focuses on the fact that the legal regime of digital financial assets is based on the legal regime of nondocumentary securities. The article makes general comments on the statuization of digital financial assets; identifies expert positions on the legal regimes of digital financial assets and non-documentary securities; examines doctrinal ideas about the relationship between the legal statuses of digital financial assets and non-documentary securities. As conclusions, the author made the following conclusions: a) the independent legal status of digital financial assets is mainly due to political motives; b) the general similarity of digital financial assets and nondocumentary securities is in the rights they certify; c) the differentiation of the phenomena being compared can be carried out on the basis of two criteria: 1) the presence/absence of an intermediary; 2) the architecture of the information system that makes up the infrastructure of the corresponding value.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".