The Elephant in the Dark: A New Framework for Cryptocurrency Taxation and Exchange Platform Regulation in the US
Bibliographic record
Abstract
The proliferation of cryptocurrencies and the remarkable expansion of novel economic practices associated with them pose an unprecedented challenge to established norms of taxation and market regulation. Drawing on two years of fieldwork, surveys, as well as big data analysis of the most valuable 100 cryptocurrencies’ white papers and the terms of service agreements of all cryptocurrency exchange platforms, this paper proposes an evidence-based framework to design a novel regulation and taxation approach to cryptocurrencies and their markets by using the US as case study. This new framework calls for approaching cryptocurrencies as data money. Drawing on the material political economy of new digital financial practices, the paper locates the universe of taxable events and invisible/vague regulation areas by approaching exchange platforms as stacked economization processes. We need to make sense of these new economic spaces in order to imagine more effective regulative instruments addressing questions of economic actor protection and efficiency. The paper concludes by proposing a new instrument of taxation (Data Money Tax) and a dynamic regulative approach to cryptocurrency exchange platforms (Stack Regulation).
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".