The Evolution of Nonfungible Tokens: Complexity and Novelty of NFT Use-Cases
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
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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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it