NFTs: Tulip Mania or Digital Renaissance?
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.005 |
| 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