Constructing digital assets through blockchain technologies? Unpacking the techno-economic configuration of non-fungible tokens
Bibliographic record
Abstract
Non-fungible tokens (NFTs) are novel techno-economic configurations underpinned by cryptocurrency ledgers that transform digital files like graphic art, music, videos, etc. into digital assets. NFTs are often framed as a way for artists and other creators to profit from their activities, transforming 'experiences' into something for sale. As such, NFTs raise some questions pertinent to science and technology studies and political economy. We focus on analysing how NFTs are constructed as digital assets by unpacking the practices, devices, relations, and rights implicated in their construction. We use the concept of 'assetization' to examine the contingencies, problematics, and implications of NFTs and the claims, practices, and entitlements that configure them as a new type of asset. We undertake this analysis through a research-creation process by summarizing and discussing the process of creating and submitting an NFT to a specialized marketplace.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".