An Ethical Roadmap for Engineering Participatory Design and Sociotechnical Participation: A Manufacturing Case Study
Bibliographic record
Abstract
Participatory design (PD) is a sociotechnical approach grounded in mutual learning between various stakeholders in a design process. The PD literature emphasizes that authentic participation requires a critical ethical foundation, which, in turn, requires designers to be aware of this ethical foundation and bring it to bear on the design process. Since this is an emerging field in engineering, and since the ethical foundation is critical, it is important for engineers to seek clarity around the ethical considerations for utilizing PD and other sociotechnical methods involving participation. The purpose of the research presented here is to contribute to this clarity, in the context of manufacturing systems design, with the following question: what are the ethical considerations involved in participatory design, in engineering research and practice? To answer this, a case study research methodology is positioned as a nexus between research and practice. A roadmap of ethical considerations relating PD and manufacturing is developed by triangulating between internationally accepted research ethics principles, a professional engineering code of ethics, and an industrial case study with eight participants engaged in PD. This ethical roadmap is useful to engineering researchers and practitioners when using PD and sociotechnical approaches where participation is involved, to encourage a high standard of ethical practice and supporting theory.
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.
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 | 0.082 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".