Society 5.0: Internet as if People Mattered
Bibliographic record
Abstract
While the primary focus of 5G has been on industry verticals, future 6G mobile networks are anticipated to become more human-centered. Emerging cyber-physical-social systems (CPSSs) aim at functionally integrating human beings into today's cyber-physical systems at the social, cognitive, and physical levels. CPSSs are instrumental in realizing the human-centered Society 5.0 vision. Society 5.0 envisions human beings increasingly interacting with social robots and embodied artificial intelligence in their daily lives. In this article, we build on our recent work on robonomics in the 6G era. Robonomics is an emerging field that investigates social human-robot interaction and its sociotechnical impact as well as blockchain technologies and cryptocurrencies, not only coins but - more interestingly - also tokens. Specifically, we study the tokenization process of creating tokenized digital twins of assets and access rights in the physical and digital world, paying close attention to its central role in ushering in the future Web3 and its underlying token economy, the successor of today's information and platform economies. After introducing our CPSS-based bottom-up multilayer token engineering framework for Society 5.0, we experimentally demonstrate how the collective human intelligence of a blockchain-enabled decentralized autonomous organization can be enhanced via purpose-driven tokens.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".