Building soil by building community: How can an interdisciplinary approach better support community needs and urban resilience?
Notice bibliographique
Résumé
Given the interrelated problems of climate change, energy and resource scarcity, and the challenge of supporting critical natural systems in cities, urban dwellers may be exceptionally vulnerable to the impacts of climate change. While a number of programs and policies have been developed and implemented to help reduce the environmental and social impacts of climate change on communities, we argue that effective and sustainable programs must not only consider how the changing environment impacts communities, but also how communities interact with and impact the environment. Specifically, drawing on a case study of the needs assessment of the Healthy Soils for Healthy Communities Initiative conducted in Los Angeles (LA) County, CA as a model for a Virtuous Cycle Framework, we attempted to better understand how urban residents interact with land, green spaces, and soil as a means of finding ways to address some of the environmental and health disparities that many urban residents experience, while also exploring ways to improve soil health to support its capacity to provide essential ecosystem services (e.g., carbon sequestration, water filtration, food and biomass production). A unique feature of our approach is that it involved an interdisciplinary and multi-level partnership composed of a well-established environmental organization dedicated to urban forestry, environmental justice, and climate resilience, university faculty researchers who study human behavior and human-nature relationships, government partners, and, most importantly, community members, among others. The first step in understanding how community members interact with their environment involved collecting survey and focus group data from residents of LA County to assess attitudes, beliefs, and behaviors around land and soil. Results were used to explore strategies for deepening community engagement, addressing knowledge gaps, and shaping policies that would benefit not just people who live/work in LA, but also the soil and other natural systems that rely on soil. This article integrates our previously published survey and focus group findings with new results that pertain specifically to the Virtuous Cycle Framework, and demonstrates how the data are being used to inform our community-based interventions (e.g., policy change, public education and community engagement, and demonstration projects).
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».