Improving cross-sectoral collaboration towards urban nature-based solutions: insights from a participatory workshop
Bibliographic record
Abstract
Nature-based solutions (NbS) have garnered attention as a vital approach to tackling the complex challenges of biodiversity loss, climate change, and human well-being in urban areas. With growing local and international support for urban NbS, professionals in diverse roles face barriers to cross-sectoral collaboration despite increasingly being asked to integrate complex information to deliver clear and innovative goals. We gathered NbS actors working in government, academia, and nongovernmental organizations for a 2-day workshop to identify (1) existing barriers and (2) leverage points to enhance collaboration within and across sectors. Led by two facilitators, participants engaged in activities designed to enable discussion and co-creation. Through a qualitative thematic analysis, our team identified several common truths experienced across sectors, including the importance of soft skills, lack of time, resources and institutional support, and increased need for Indigenous-led work. We also discuss six broad themes related to sectoral changes: rethinking academic incentives and enduring cultures, building horizontal bridges in government, promoting Indigenous leadership, trust-building in municipal government, NGO leadership roles, and expanding opportunities for knowledge-sharing across all sectors. We outline leverage points and provide recommendations to improve cross-sectoral collaboration for urban NbS.
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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 | 0.070 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".