Human‑centric Industry 5.0 manufacturing: a multi‑level framework from design to consumption within Society 5.0
Bibliographic record
Abstract
Current literature on human-centric Industry 5.0 manufacturing largely focuses on operator–technology integration at manufacturing process level, often treating ‘human’ as synonymous with ‘operator’ and overlooking designers, consumers, and public non-consumers. Links between human-centricity with sustainability, resilience, and circular economy are poorly developed, and connections to Society 5.0 values remain weak. This perspective proposes a hierarchical framework for human-centricity in Industry 5.0 across manufacturing process, system, and management levels. At the process level, it addresses worker safety, occupational health, human-robot-collaboration, and customer co-creation, customisation, and personalisation. At system level, it integrates ergonomic layout design, human-centred logistics and production planning, and resource execution. At management level, it emphasises ethical business practices, inclusive workplace culture, and corporate social responsibility. The framework merges Industry 4.0 tools (e.g. digital twins, AI, IoT, blockchain) with the active role of Consumer 5.0 for balancing consumption and production in sustainable manufacturing, while detailing circular economy practices across manufacturing stages, including design for reuse, remanufacture, and recycling. By connecting diverse human roles with key Industry 5.0 pillars and Society 5.0 principles, we avoid fragmented solutions for human-centric manufacturing. While discussing its social, economic, and technological limitations, we offered a comprehensive framework, which is technologically innovative, socially responsible, and environmentally sustainable.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".