What's Next in Blockchain Research?
Bibliographic record
Abstract
Distributed ledger technology, frequently designated as 'blockchain,' is evolving from its hype phase toward greater maturity and long-term value creation. Although many academic communities were initially slow to grasp the technology's numerous potential implications, meanwhile a substantial amount of research is dedicated to investigating the development and impact of blockchain and related technologies. As undertaken, most research projects take a specific homogenous perspective, such as a technical or business viewpoint. To date, blockchain research studies are largely missing a bridge between and across academic disciplines. Given the manifold implications of blockchain technology, a fruitful crossdisciplinary exchange is therefore needed. In this paper, we bring together researchers with varying expertise to provide a vision into what may be next in terms of concepts, applications, and research agendas. We consider business, economic, societal, legal, technical, and philosophical viewpoints and propose multiple research questions as well as hypotheses arising from these diverse viewpoints. Simultaneously, we challenge various academic communities to tackle some of the most crucial issues of current blockchain research and to develop a solid foundation for future exploration.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.026 | 0.064 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".