Bibliographic record
Abstract
Abstract The business model of crypto custodians is relatively new. If these companies fall into financial distress, the question arises as to which legal framework is applicable to them. Since jurisdictions such as the US, the Swiss, the German, and recently also the European Union place crypto custodians under financial supervision, it seems reasonable to assume that the numerous European legal acts for these firms and the recovery and resolution of credit institutions, investment firms and other firms may be relevant (SRMR, BRRD, MiFID II, CRR, MiCAR etc). On the other hand, crypto custodians could be coherently located in the system of European insolvency law. However, the EIR Recast contains an exclusion for certain companies in the financial sector. Having now seen major crypto custodians in financial distress, legal scholars must answer the question of whether one of the legal frameworks is applicable to crypto custodians or whether the European legislature must extend the scope of one of the regimes to include crypto custodians. The study will show that the business model of pure crypto custodians holding crypto currencies in custody is not covered by major European regulations and directives concerning the financial sector but can be covered by the EIR Recast through a narrow interpretation of its scope exclusion. Taking the European legislator's perspective, the paper demonstrates that neither the CRR, SRMR, nor BRRD will lead to coherent results with respect to crypto custodians in financial distress but instead, though unintentional, the application of the EIR Recast. Concerning crypto custodians, the EIR Recast, therefore, seems to be the more suitable regime.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".