Outlook on human-centred design in industry 5.0: towards mass customisation, personalisation, co-creation, and co-production
Bibliographic record
Abstract
This study reviews and expands upon the traditional human-centred design within the emerging paradigm of Industry 5.0. It differentiates human-centred design from user-centred approaches by advocating beyond usability encompassing social, environmental, and ethical considerations. The framework considers humans at multiple levels and explores how designers can benefit from Industry 4.0 technologies to interact with non-designer humans. It examines human-centred design approach in co-creation and co-production practices. Furthermore, it investigates the role of technological enablers (artificial intelligence, digital twins, computational design, digital fabrication, robotics, augmented/virtual reality), in facilitating communication and collaboration throughout the design process from observation and ideation to prototyping and testing. The framework leverages a smart data-centre to ensure interconnectivity and integration across all stages. By incorporating technological enablers of Industry 4.0 and addressing the concerns and values of Industry 5.0, we explore human-centricity at both the consumption and production levels, ultimately fostering sustainable, ethical, and socially responsible design practices. Successful implementation of human-centred design requires addressing several challenges related to data security, transparency, and explainability focusing on technological scalability, and stakeholder engagement. Investing in developing industry-wide standards, open-source solutions, and digital literacy initiatives is crucial to keep human-centric design at the core of Industry 5.0 advancements.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".