Building soil by building community: How can an interdisciplinary approach better support community needs and urban resilience?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".