Bibliographic record
Abstract
This paper investigates non-fungible tokens, or NFTs, and examines their place within art historical canon. Crypto-art and crypto-collectibles have flooded digital markets, offering unique art. Recently, Beeple’s Everydays—The First 5000 Days, the first digital artwork fitted with a non-fungible token offered by the major auction house Christie’s, sold for $69,346,250 on March 11, 2021. It is the third most expensive artwork sold by a living artist, following Jeff Koon’s sculpture Rabbit (1986) and David Hockney’s painting Portrait of an Artist (Pool with Two Figures) (1972). While Jeff Koons and David Hockney are the artists, whose theoretical perspectives are well known and have a secured place in an art historical canon, Beeple’s work and works of other digital NFT artists have not been fully investigated to be positioned in relation to art history, seemingly existing in a theoretical vacuum. The absence of artistic statements that usually accompany artworks contributes to this effect. Is it possible to think of the 21st century NFT-backed digital artists as the avant-gardes, who, like their 20th -century predecessors, confronted and condemned the art historical tradition? Using the case study of Beeple’s Everydays, this paper proposes an answer to the puzzling question of how a mosaic of everyday sketches produced by “pooping something out in 45 minutes,” using Beeple’s own words, was claimed to be “the next chapter in art history.” Using historical and textual analyses, this essay provides a critical response to the recent digital artworld trends driven by the decentralized networks and currencies existing in fully digital ecosystems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".