Co-producing new knowledge systems for resilient and just coastal cities: A social-ecological-technological systems framework for data visualization
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle