Bibliographic record
Abstract
This article explores the legal and tax landscape applicable to non-fungible tokens (NFTs)—first, by providing a working definition of an NFT and explaining how NFTs work from a technical perspective, with real-world examples; and second, by exploring the unique income tax issues raised by NFTs as a class of cryptoassets separate from their fungible counterparts. Key features of NFTs are found to be (1) their non-fungible nature; (2) their role as a digital record of authenticity of the ownership of tangible or intangible assets, but not of the existence of the underlying assets; and (3) the ability to integrate further rights for creators, such as commission rights for each sale of the NFT by subsequent owners. The article posits that NFTs may not themselves constitute an actual property right, since the underlying assets are very rarely included in the NFT record. A working definition of an NFT is therefore proposed to be as follows: a blockchain-based record of authenticity of rights to a unique object that contains metadata about the object that it represents and a link to the location where the object or data about the object are stored. The purchase and sale of an NFT is a taxable event, whether made by the original creator (who minted the token) or a subsequent owner. The determination of whether the transaction is on income or capital account, whether the NFT is personal-use property, and whether other rules apply (for example, non-resident withholding tax rules) will depend on the exact nature of what is being sold. The determination of the source of the income and the application of treaties to exempt income is quite difficult. Also raising difficulty is the qualification of the commission that is earned on subsequent sales of the NFT.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".