Advancing environmental justice in cities through the Mosaic Governance of nature-based solutions
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".