Unleashing collective imagination through controversies: lessons from a smart city project
Bibliographic record
Abstract
We explore the role of futures-oriented design interventions in leveraging socio-technical controversies to foster collective imagination. We elaborate on a practical application of a speculative and scenario-based design tool called Future Frictions. Our study focuses on the use of Future Frictions to engage citizens in the development of an assessment framework for implementing sensors in Amsterdam. By employing the "controversing" framework to operationalize controversies through design, we explore how Future Frictions provides an interface that bridges speculative and real-life urban contexts. This interface facilitates recontextualizing controversies in daily life, fostering sensemaking, and making space for collective agency. This, we argue, nurtures collective imagination to generate counter-narratives that open alternative smart city futures. In addition to contributing to responsible smart city developments, we offer inspiration for utilizing design to reimagine and deploy creative forms of engagement to inform decision-making and policy-making addressing societal challenges in different domains.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.038 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".