Rethinking the marginal: service design for development
Bibliographic record
Abstract
This article is asking how service design can be used for development and improving livelihoods in the margin? The margin can be geographical but is can also be created around complex wicked problems that have resulted from historical or societal reasons. The article presents two cases studies where the margin is constructed around two different situations. The first case is looking at the arctic context of Lapland where geography causes marginalization and isolation. This case study is connected with “IKÄEHYT” project focusing on designing services with the elderly. The project was run in 14 Lappish communities to increase accessibility, inclusion and wellbeing. The second case is looking at indigenous San communities in South Africa where historical and societal context is creating a challenging situation for the local youth. The second case study is presenting ”PARTY” project (Participatory Development) with youth where service design is used to increase the youth participation in open democratic society and in designing their services. The article has a strong social design ethos. The article gives a brief overview on social design debates and uses that as a framework to analyze the case studies. Both case studies contribute to the rethinking of the marginal, how it is constructed and how service design can be used to create new solutions to overcome the challenges.
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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 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".