Designing in between Local Government and the Public – Using Institutional Analysis in Interventions on Civic Infrastructures
Bibliographic record
Abstract
Adapting and changing the systems and technologies involved in civic engagement with local government is among the key challenges of collaborative technologies for political participation. In such contexts, both existing sets of technologies and ingrained, often formalised practices, the ‘rules of the game’, constrain any opportunity for intervention. Additionally, ‘civic’ and expert groups with conflicting agendas and divergent demands on public choices assert their influence in these transformation programmes. The article argues that established methods in collaborative systems design have thus far overlooked the role of recurring actions involved in public participation as well as the formal rules and ingrained practices that construct them. Yet, such patterns present a valuable resource for design interventions. Thus, based on an institutional approach, the article outlines a methodology for requirement gathering by mapping the relations of actors, software and their use along identifiable action situations. The method called for a dialogue between socio-technical-spatial contexts of public service and specific actions taking place within it. Drawing on a case of organising civic engagement in urban planning, the article discusses how to find and trace existing practices across social settings, information technologies and material contexts where engagements take place. The approach underscores the existing institutional contexts in inspiring, opening and constraining the opportunities to support ‘civics’.
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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 | 0.049 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| 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".