Co-producing new knowledge systems for resilient and just coastal cities: A social-ecological-technological systems framework for data visualization
Bibliographic record
Abstract
With increasing frequency and severity, coastal cities are facing the effects of extreme weather events, such as sea-level rise, storm surges, hurricanes, and various types of flooding. Recent urban resilience scholarship suggests that responding to the cascading complexities of climate change requires an understanding of cities as social-ecological-technological systems, or SETS. Advances in data visualization, sensors, and analytics are making it possible for urban planners to gain more comprehensive views of cities. Yet, addressing climate complexity requires more than deploying the latest technologies; it requires transforming the institutional knowledge systems upon which cities rely for preparation and response in a climate-changed future. While debates in the theory and practice of knowledge co-production offer a rich contextual starting point, there are few practical examples of what it means to co-produce new knowledge systems capable of steering urban resilience planning in fundamentally new directions. This paper helps address this gap by offering a case study approach to co-producing new knowledge systems for SETS data visualization in three US coastal cities. Through a series of innovation spaces – dialogues, labs, and webinars – with residents, data experts, and other city stakeholders from multiple sectors, we show how to apply a knowledge systems approach to better understand, represent, and support cities as SETS. To illustrate what a redesigned knowledge system for urban resilience planning entails, we document the key steps and activities that led to a new prototype SETS platform that works with a wider range of ways of knowing – including community-based expertise, interdisciplinary research contributions, and various municipal actors' know-how – to build anticipatory capacity for visualizing and navigating the complex dynamics of a climate-changed future. Our findings point to new roles for activity-based learning, conflict, and SETS visualization technologies in connecting, amplifying, and reorganizing the knowledge assets of community perspectives previously ignored. We conclude with a new understanding of how innovation towards coastal city resilience resides within the co-production process for (re)designing knowledge systems to make them more robust and responsive to cross-sector and cross-city learning. • Three US coastal cities experiment in co-producing new knowledge systems using innovation spaces. • Co-produced knowledge systems are more inclusive, connected, and anticipatory than conventional city knowledge systems. • A prototype visualization platform supports and sustains a networked approach.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".