Conclusion: possible directions of blockchain
Bibliographic record
Abstract
In this book, we have provided a comprehensive overview of the phenomenon of blockchain technology, its potential and its pitfalls. Blockchain technology holds significant disruptive potential for changing the way individuals, companies and authorities interact. Although Bitcoin was invented more than a decade ago, the innovative use of the technology that undergirds it has only recently begun and can still be regarded as being in its infancy. However, blockchain technology has to overcome some challenges and shortcomings in the near future for its full potential to be rolled out and for it to outperform existing centralized systems. Other disruptive technologies, such as the internet, provide a paradigm for blockchain. While the basis of these technologies, the Arpanet, was already in operation in October 1969, it took many years until standards such as TCP/IP were established in 1981, and in 1989 the foundations of the World Wide Web were laid. The first browser was published in 1993 with Mosaic. From this point on, it was only through applications such as email that the internet actually began to be used extensively. Consequently, new, complex technologies that can change or displace the business models of established companies are not necessarily successful in the short term. We are at an early stage of research and experimentation with blockchain. While particular sectors can realize the benefits of the technology faster than others, various challenges still have to be overcome before standards are established and the technology can be applied safely and extensively. The challenges for blockchain are technical, governmental and regulatory. The technical issues relate to the scalability of public blockchain systems. The price for the decentralization is that processing tasks need to be performed redundantly, as every validator node needs to process every transaction. The securitization of the system using complex consensus algorithms results in low transaction throughput, which makes them less competitive compared to conventional payment services like Visa. Moreover, block formation intervals are comparably long (13 seconds on average for Ethereum transactions, 10 minutes on average for Bitcoin transactions). These features are impractical and hinder its widespread adoption as a means of payment or for the use of time-critical applications.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".