Bibliographic record
Abstract
NFT, as a trending product in the digital media industry, has received much attention and market volumes starting from early 2021. The increasing popularity of NFT products and potential suggests that thorough research and development generate effective marketing strategies in response. Hence as a group project, we designed and implemented gamification features to NFTs to increase user engagement and fun experience while investing in such digital assets. This MRP focuses on the technology and implementation side of the project. For design and marketing, please refer to Yanbo Xing's MRP. The methodology used in this project is to evaluate reflection and iteration.After the literature review, the NFT market potentials, cryptocurrencies, mainstream token standards, and project case studies were researched and analyzed. Drawing from the reflection of initial research, design decisions were made. In execution, our project applied the design decisions and iterated from current NFT projects to compile our smart contracts to fulfill the gamification characteristics. The result is a gamified NFT loot box product prototype. Our research proves the feasibility of creating a smart contract for gamified NFT products to increase user engagement. Optimizing codes and applying such a framework to other industries will be the future goals.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".