A First Glance at the NFT Quebec Gaming Scene
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
This article examines the discourses surrounding non-fungible tokens (NFTs) in gaming and identifies companies involved in NFTs in the Quebec gaming scene. NFTs boomed in the gaming industry in 2021 and continued to grow in 2022, even as the value of gaming coins plummeted. If successful, some believe they could bring new opportunities to the gaming landscape. We conducted an online ethnography in early 2022 through an innovative web-scanning approach and curation process powered by a professional market intelligence platform. Data was collected from various sources and analyzed via statistical analysis software to understand the discourses of companies, gamers, researchers, and insiders. Findings show that the technical and economic discourse is at least ambivalent if not negative, while the gamer discourse is mostly negative. The burgeoning Quebec scene is currently very limited and divided into two groups: large gaming companies and startups. Despite the crypto-enthusiast craze, our analysis shows that early projects were often criticized by traditional gamers and that professionals in the sector remain skeptical.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it