Exploring Non‐Fungible Tokens: A Bibliometric Analysis and Future Research Opportunities
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
ABSTRACT Non‐fungible tokens (NFTs) are digital assets that represent the ownership of unique items that can be bought or sold using cryptocurrency. This comprehensive analysis of NFT involved a literature search conducted in February 2025. A total of 963 publications were initially identified from the Scopus database. Following meticulous screening, analysis, and evaluation, 734 relevant and high‐quality documents were selected for further examination, employing rigorous discussions, voting, and critical appraisal. The literature review on NFTs highlighted several frequently co‐occurring keywords, including Blockchain, Smart Contracts, Commerce, Ethereum, Digital Assets, Metaverse, Decentralization, Digital Storage, Cryptocurrency, and Distributed Ledger. This study organizes NFT research into 10 distinct categories through a combination of review and text mining techniques. These categories include “pricing, marketing, and investment,” “application of NFTs,” “art,” “games and metaverse,” “benefits, drawbacks, and review papers,” “security”, “law and ownership,” “system for NFT and extending of NFT,” “Supply chain management,” and “AI.” For each category, the research questions and their corresponding answers are mapped. Additionally, the study developed a comprehensive framework to establish connections between research categories, providing valuable insights into expanding NFT adoption. Finally, this research investigates the challenges associated with the application of NFTs and explores potential future research directions.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.067 | 0.040 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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