The Evolution of Nonfungible Tokens: Complexity and Novelty of NFT Use-Cases
Bibliographic record
Abstract
Nonfungible tokens (NFTs) have recently drawn considerable attention, highlighted by a digital art piece that sold for $69M USD in early 2021. Though they have only just started receiving coverage by traditional media outlets and interest from casual observers, the foundations of NFT technology date back to advances in computer science in the late 1970s. In this article, we examine the emergence of NFTs, from their technical origins, the introduction of blockchain technologies and the first token-based collectibles that led to modern day NFT products. We categorize the current use cases for NFTs, introduce their potential future applications, and highlight the challenges managers face in incorporating them into their existing workflows. By presenting our NFT adoption framework, we offer managers strategies for evaluating the risks and benefits of NFTs.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".