Anti-racist, decolonial, and transdisciplinary approaches for nature-based solutions that benefit biodiversity and human well-being
Bibliographic record
Abstract
Abstract There is growing recognition of the important role that nature-based solutions (NbS)—interventions that protect, manage, and restore ecosystems to address societal challenges—can play in curbing biodiversity loss, combating climate change, and contributing to human wellbeing. However, successfully implementing NbS to balance co-benefits in cities remains challenging. Given the recent proliferation of local, national, and global policies and targets around urban NbS, ensuring successful outcomes of these interventions is critical. This paper introduces a special issue focused on studies that interrogate the process and initial outcomes of several urban NbS projects across scales and geographies, treating cities as complex social-ecological systems. We highlight cross-cutting themes that emerge from this collection of five papers. To better understand and maximize the co-benefits of NbS, we must be prepared to: (1) integrate a wide range of disciplines and methodologies; (2) acknowledge and support the role of local stewardship; (3) deeply consider the role of participation and co-production; (4) re-focus efforts through an anti-racist and decolonial lens, and; (5) better understand the processes underlying the generation of co-benefits. Critically, these papers emphasize the fundamental role of equity-, social justice-, anti-racist-, and decolonial-approaches in successful NbS programs.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".