Bibliographic record
Abstract
At Bitcoin’s peak in November 2013, there were 93,000 global transactions made in a single day. These users purchased everyday items such as personal services, food, and real estate. This alone suggests that Bitcoin is not primarily used as a long-term investment tool, but rather is used as a currency and a vehicle for global transactions. Congress and the IRS should regulate it accordingly. Representative Stockman’s Virtual Currency Reform Act offered an attempt to negate the IRS decision and officially classify Bitcoin and other virtual currencies as currency instead of property. A tax reclassification would alleviate typical users’ many inconveniences caused by burdensome accounting and tax reporting. A reclassification would also allow and encourage the use of Bitcoin and other virtual currencies because imposing a sales tax on transactions similar to everyday currencies is a small change that most users would not find prohibitive or restrictive. While it is evident that there needs to be some form of IRS taxation of virtual currencies, attempting to classify Bitcoin according to existing tax principles is challenging and ineffective.\nAlthough this is new technology and subsequently uncharted territory for many doctrines of law, the technology should be embraced and encouraged to prosper. For example, typical sales tax on transactions made on the internet are currently an unsolved dilemma. It gets even trickier trying to throw virtual currencies into the mix. Between complex tax law, jurisdictional issues, and the constant globalization of our economy, challenging legal questions will arise. Classifying certain Bitcoin transactions for a sales tax instead of a capital gains and losses tax is the first step in the right direction toward answering these difficult questions and encouraging the use of Bitcoin and other virtual currencies to further global trade in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".