Bibliographic record
Abstract
Web 3.0 has revolutionized the internet with its salient features of decentralization, trustful and permissionless, artificial intelligence and machine learning, connectivity, and ubiquity. Although the idea of Web 3.0 gained much popularity in 2021, the term was coined in 2014 by Gavin Wood. The advent of Web 3.0 has ushered in a new era of blockchain technology, and this chapter is an attempt to unleash the tremendous potential of this technology with a prime focus in the education sector. Although the fields in which this technology has contributed are wide and diverse, the innovative potential of Web 3.0 in the education field is explored in this chapter and its future implications are assessed and analyzed. There has been a profound impact of Web 3.0 in education services and the amalgamation of Web 3.0 technologies has altogether transformed the education sector and its contribution to society is undeniably important. In adherence to this, this chapter unravels the transformations caused by Web 3.0 in the teaching-learning process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.210 | 0.278 |
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".