Human‑centric Industry 5.0 manufacturing: a multi‑level framework from design to consumption within Society 5.0
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it