Whom do we include and when? participatory design with vulnerable groups
Bibliographic record
Abstract
This article makes three contributions to participatory design (PD) research and practice with vulnerable groups: 1) a framework for understanding stakeholder engagement over the course of a PD project; 2) approaches to making user engagement and PD activities more inclusive; and 3) an analysis of how the design and power dynamics of PD projects affect vulnerable groups’ participation. A map of engagement is developed to evaluate stakeholder involvement from initial problem definition to design outcome. The map is applied to three projects aimed at increasing inclusion of vulnerable groups in the planning of public sector services. The first looks at codesign activities to support decision-making in the context of intellectual disabilities; the second looks at culturally diverse youth navigating crisis without adequate assistance from public services; and the third examines nursing students adapting to work in the health sector without accommodations for learning disabilities. Comparing the projects reveals patterns in project planning and execution, and in stakeholder relationships. The article analyses how users are defined, engaged and supported in PD; how proxies shape vulnerable groups’ involvement and PD projects as a whole; and opportunities for greater inclusion when the entire PD project is taken into account.
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 | 0.090 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".