Designing the Future? The Metaverse, NFTs, & the Future as Defined by Unity Users
Bibliographic record
Abstract
The “metaverse” and non-fungible tokens (NFTs), though not necessarily “new” terms or technologies, have risen to mainstream prominence post-2020. This paper, based on survey data obtained from Unity Technologies, examines the metaverse, NFTs, and the future of development within the Unity engine from the perspective of current Unity users. Specifically, the paper examines how users define the metaverse, their goals in metaverse and NFT development, and their future questions and concerns concerning these concepts. This data is then used to place the metaverse and NFTs into broader historical, present, and future contexts. The paper ultimately argues: (1) the metaverse and NFTs follow previous historical trends in communication technology development, (2) development within Unity will continue to be split between game development and non-game development, and (3) arguments of the “newness,” “uniqueness,” or “future-facing” of the metaverse and NFTs help to obfuscate legitimate concerns about these technologies.
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.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".