Transformation of blockchain and opportunities for education 4.0
Bibliographic record
Abstract
There are many ways in which education 4.0 can continue to develop rapidly and coexist with the development of increasingly advanced technology to bond with each other to be balanced, one of which is using blockchain technology that is integrated in the education sector for various purposes. The main direction in developing global integration in education includes the creating a single educational space and optimization of the interaction between education and stakeholder relationships. The blockchain method implemented in education 4.0 was not widely used because initially, blockchain was only known for the financial sector. Blockchain is comprehensive and appropriate for this era, as blockchain offers technology, trust, and transparency by replacing the previous system with a new system. A particular problem is a need for innovative research to provide new insights into blockchain transformation inf education and application opportunities that can be accepted and used optimally. Researchers used the vast mind method and literature study. The goal is to inform the added value of blockchain that is applied in education as needed, the renewal of research and opportunities for implementing blockchain in education 4.0. The blockchain technology that can be used in education, for example, is archiving, learning, certification and other.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".