Non-Fungible Tokens (NFT): A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-fungible tokens (NFTs) are gaining in popularity and are already extensively implemented. New use cases for NFTs are constantly developing. NFTs may prevent counterfeiting since each token carries the owner’s digital signature and is thus unique. For the usage of NFTs to progress in an institutional environment, the potential for using NFTs must be investigated in detail. This discovery prompted a comprehensive examination of NFTs developed between 2012 and 2022. The scope is confined to the journal and the keywords “Blockchain”, “Block-chain”, “Non-fungible Token”, and “NFT” are used. Also excluded are studies based on interviews, articles in the press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs. This evaluation includes 34 papers from the last decade. This research examines the current state and development trends of NFT. In addition, the gaps and difficulties in the related literature have been explored, with an emphasis on the limits. These results highlight many unsolved research questions and potential future research avenues that would likely be beneficial to academics and professionals.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it