Bibliometric Analysis of Articles on Web3
Bibliographic record
Abstract
In this study, it is aimed to analyze the articles on Web3 and present the general situation about Web3 to researchers. Within the scope of this purpose, the trends of the studies published on Web3 according to years, the trends of the journals in which they were published, the institutions and countries that contributed the most, the keywords used in the studies, the topics and themes based on the studies, and the distribution of research areas were revealed. The research is based on bibliometric analysis. A total of 280 articles published in WoS and SCOPUS databases were analyzed. WoSViewer and Bibliometrix programs were used in data analysis. The findings were analyzed and interpreted separately in WoS and SCOPUS. As a result of the research, there was a significant increase in studies on Web3 in 2022, and the journals with the highest number of publications in WoS and SCOPUS differ. The countries that contributed the most to Web3 were China, The USA, India, England, Germany. The most cited countries are China, the USA, India, England, Iran and Canada. In general, it can be said that countries and institutions have conducted studies on Web3 by addressing many issues related to Web3. Within the scope of the results, Web3 studies address many different disciplines with many topics. However, there is a need to deepen the studies. The policies, practices and even the laws created by countries on Web3 are important for studies on Web3. Blockchain is one of the most studied topics, but it is understood that there are some hesitations about blockchain security. For this reason, Web3 studies can be conducted to increase blockchain security.
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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.180 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".