Activating Data through Eco-Didactic Design in the Public Realm: Enabling Sustainable Development in Cities
Bibliographic record
Abstract
This paper explores how design in the public realm can integrate city data to help disseminate the information embedded within it and provide urban opportunities for knowledge exchange. The hypothesis is that such art and design practices in public spaces, as places of knowledge exchange, may enable more sustainable communities and cities through the visualization of data. To achieve this, we developed a methodology to compare various design approaches for integrating three main elements in public-space design projects: city data, specific issues of sustainability, and varying methods for activating the data. To test this methodology, we applied it to a pedogeological project where students were required to render city data visible. We analyze the proposals presented by the young designers to understand their approaches to design, data, and education. We study how they “educate” and “dialogue” with the community about sustainable issues. Specifically, the research attempts to answer the following questions: (1) How can we use data in the design of public spaces as a means for sustainability knowledge exchange in the city? (2) How can community-based design contribute to innovative data collection and dissemination for advancing sustainability in the city? (3) What are the overlaps between the projects’ intended impacts and the 17 United Nations Sustainable Development Goals (SDGs)? Our findings suggest that there is a need for such creative practices, as they make information available to the community, using unconventional methods. Furthermore, more research is needed to better understand the short- and long-term outcomes of these works in the public realm.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".