Advancing environmental justice in cities through the Mosaic Governance of nature-based solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nature-based solutions (NBS) are championed for providing co-benefits to cities and residents, yet their environmental justice impacts are increasingly debated. In this paper, we explore whether and how hybrid governance approaches, such as Mosaic Governance, may contribute to just transformations and sustainable cities through fostering long-term collaborations between local governments, local communities, and grassroots initiatives. Based on case studies in three major European cities, we propose and then exemplify six possible pathways to increase environmental justice: greening the neighborhood, diversifying values and practices, empowering people, bridging across communities, linking to institutions, and scaling of inclusive discourses and practices. Despite the diversity of environmental justice outcomes across cases, our results consistently show that Mosaic Governance particularly contributes to recognition justice through diversifying NBS practices in alignment with community values and aspirations. The results demonstrate the importance of a wider framing of justice in the development of NBS, sensitive to social, cultural, economic and political inequities as well understanding potential pathways to enhance not only environmental justice, but also social justice at large. Especially in marginalised communities, Mosaic Governance holds much potential to advance social justice by enabling empowering, bridging, and linking pathways across diverse communities and NBS practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it