Editorial: Blockchain Ecosystem—Technological and Management Opportunities and Challenges
Bibliographic record
Abstract
Blockchain is increasingly deployed in a broad range of sectors, ranging from banking and finance to manufacturing to energy to transportation, and so on. While many technological and business related blockchain developments and challenges have been identified, many of these engineering and management challenges have not been addressed. The ongoing interest in this topic is also partly evidenced by the large number of submissions we received in this special issue. Of the 200 submissions, only 39 articles were eventually accepted after several rounds of rigorous reviews (i.e., acceptance rate of 19.5%). In this editorial, we report on the findings from the first 36 articles on a broad range of topics (e.g., supply chain, financial technology, Internet of Things, smart city, healthcare, security, privacy, and blockchain building blocks such as consensus algorithms). Hopefully, the findings reported in these 36 accepted articles will provide sustainable solutions for existing and future blockchain systems and platforms.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".