Everyday People: Enabling User Expertise in Socially Responsible Design
Bibliographic record
Abstract
This paper examines the contemporary relevance of interdisciplinary research practice specifically within the field of design for social need. Examining the complexity of current social problems using the concepts of Rittel & Webber’s wicked problems, this paper looks at the potential for the application of co-design methods within an interdisciplinary framework. By proposing the use of a social model of design, it is argued that it is through co-design methods and the use of generative toolkits such as Liz Sanders’ MakeTools and IDEO’s Human-Centered Design Toolkit that the design process can be enhanced in the early stages. This paper argues for interdisciplinary practice by enabling user expertise so that the user can equally contribute to the design process. This paper also explores the changing role of the designer from researcher to facilitator, and how this can benefit communities dealing with complex problems. Finally, this paper looks at the benefits of active user involvement in socially responsible design through discussions on empathy, user empowerment and benefits to communities within design education.
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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".