NFTs: Tulip Mania or Digital Renaissance?
Bibliographic record
Abstract
Galleries, Libraries, Archives and Museums (GLAM) institutions have begun to sell non-fungible tokens (NFTs) of works from their collections following the $69.3 M USD record sale of Beeple’s Everydays: The First 5000 Days at Christie’s auction house on March 11, 2021. But many open questions exist about whether NFTs are beneficial or harmful for such institutions from financial, regulatory, and environmental perspectives. In this paper, we aim to unpack what NFTs are within the context of tokenomics and Ethereum standards development by providing an overview of notable NFTs and selling platforms before discussing the pros and cons of their use in GLAM institutions and exploring open research challenges through the lens of Computational Archival Science. Methodologies for the creation (minting) and purchase of NFTs are provided, emphasizing NFTs’ record keeping abilities, while also highlighting their inherent vulnerabilities, particularly with regards to the now-infamous broken link problem and its implications for provenance tracking and authenticity.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".