Co-designing public services with vulnerable and disadvantaged populations: Insights from an international collaboration
Bibliographic record
Abstract
This paper presents key insights from an ongoing international collaboration designed to explore and enhance the effectiveness of citizens’ engagement in designing public services (track 4 of ServDes.2018). Eight practitioner-led case studies that draw upon service design thinking and approaches to work with vulnerable and disadvantaged populations were examined to explore the experiences of participating in projects across a range of critical social service sectors (including community services, healthcare, housing, employment support, policing, and justice) in three countries. Within and cross-case comparison led to identification of key challenges related to engagement, power differentials, health concerns, funding and other economic and social circumstances that affected meaningful, sustained participation of the stakeholder groups. At ServDes we will share the lessons learned from a co- design process involving leaders and participants in the case studies about designing improvements in public services, with particular attention to engaging vulnerable and disadvantaged populations.
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 | 0.072 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".