When Design Fiction Meets Geospatial Sciences to Create a More Inclusive Smart City
Bibliographic record
Abstract
Smart cities are especially suited for improving urban inclusion by combining digital transition and social innovation. To be smart, a city has to provide every citizen with urban spaces, public services, and common goods that are effectively affordable, whatever the citizen’s gender, culture, origin, race, or impairment. Based on two design workshops, the “Vibropod” and the “Pointe-aux-Lièvres”, this paper aims at highlighting the contributions of design fiction to the improvement of the spatial capability of hearing impaired people. This research draws its originality from both its conceptual framework, built on an interdisciplinary and intersectoral composition of arts and sciences, and its operational approach, based on the use of the DeafSpace markers and the TRIZ theory (Russian acronym for Inventive Problem Solving Theory) principles. The two design fiction workshops demonstrate that considering the singularity of the human being as an actual acoustic material constitutes an innovative opportunity to improve the role of universal design in a smart city project. By reversing the classic posture, and defining disability by looking at characteristics of the environment rather than as limits of the people themselves (their bodies or their senses), this research proposes an innovative way of addressing smart city inclusivity issues. This paper shows how increasing spatial enablement and having better control of spatial skills can offer deaf people new skills to improve the use of technology in support of urban mobility, as well as give them tools for feeling safer in urban environments.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".