Concealing More Than Your Affairs: A Deep Dive into the World of Cryptocurrency and its Future Influence on Family Law in Ohio
Bibliographic record
Abstract
This Note dives into the world of cryptocurrency and family law in Ohio. With its current popularity and dramatic fluctuations, cryptocurrency has created a new legal issue in the family law practice. Specifically, this Note focuses on the concealability of Bitcoin and how that influences division of property, spousal support, and child support in Ohio divorce proceedings and settlements. To tackle this issue, this Note begins with the history of Bitcoin, its value since the beginning, as well as the reason for its fluctuations. This Note also looks into what makes Bitcoin and other cryptocurrency forms so concealable. This Note then analyzes the current family law in Ohio, using relevant Ohio Revised Code sections as well as case law that presents examples of how standard assets are divided amongst spouses in a divorce. These statutes and cases are also used to analyze allocation of spousal and child support. This Note then looks to how Ohio has treated parties who conceal their assets in divorce proceedings with respect to division of property, spousal support, and child support. Lastly, I analyze how other states have treated Bitcoin in divorce settlements, as well as the approach countries like the United Kingdom and Canada treat cryptocurrency. This Note finds that cryptocurrency should be treated similarly to standard assets in divorce settlements, and that the Ohio Legislature should act quickly to propose amendments to relevant family law statutes to include cryptocurrency as a standard asset.
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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 | 0.004 | 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 | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".