Perspective Chapter: Nano and Society 5.0 – Advancing the Human-Centric Revolution
Bibliographic record
Abstract
The chapter “Nano and Society 5.0: Advancing the Human-Centric Revolution” delves into the profound implications of nanotechnology within the context of Society 5.0, a visionary concept that seeks to harmoniously merge technological progress with human-centric ideals. Society 5.0 envisions a world where technology enhances life quality for individuals and society, with nanotechnology playing a crucial role in this transformation. This chapter explores the role of nanotechnology in Society 5.0, highlighting its potential in personalized healthcare, real-time health monitoring, sustainability, and education. Nanotechnology enables precision medicine, enabling tailored treatments and diagnostics. It also revolutionizes energy generation, storage, and materials science, contributing to environmentally conscious construction practices. Nanotechnology-driven innovations address global challenges such as water purification and resource conservation. In education, nanotechnology inspires future generations, particularly in STEM disciplines, and supports accessible and inclusive learning environments. However, ethical considerations regarding privacy, equitable access, and responsible governance must be considered as nanotechnology becomes a central focus in this human-centric revolution. This chapter highlights the role of nanotechnology in shaping society toward a future where technology aligns with core values, demonstrating its potential to be a transformative force, propelling Society 5.0 into a new era of innovation, inclusivity, and human betterment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".