A First Glance at the NFT Quebec Gaming Scene
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".