Reflecting on Local Ecological Stewardship, Care, and Action across Two Decades of Research
Bibliographic record
Abstract
In this perspective, we draw from 20 years of implementing the Stewardship Mapping and Assessment Project (STEW-MAP) to show how civic actors provide capacity and local knowledge needed for effective decision-making and implementation in the face of multiple interconnected stressors, including climate change and inequality. Urban areas are striving to achieve sustainability and resilience goals while advancing diversity, equity, inclusion, and justice. There is broad recognition that systematic change cannot be achieved via single sector solutions. Rather, just and equitable sustainability and resilience outcomes will be achieved through multi-sector, trans-disciplinary efforts led by diverse and inclusive partnerships. Processes of collaboration between groups and across sectors can foster trust and social cohesion to build adaptive environmental governance capacity. Hindering these outcomes is a lack of approaches for identifying civic groups and their networks, understanding their roles in the larger governance system, and harnessing their capacities systematically and at landscape scales. STEW-MAP was developed to address this gap in a natural resources management context and has been applied in 20 locations across the Americas. Synthesizing key insights for practitioners and researchers, we identify the critical role of civic organizations in collaborative, networked governance, while highlighting inequities that affect this stewardship work. We reflect on how stewardship mapping has been used as a decision-support, networking, and visualization tool and identify future research and practitioner directions that fully acknowledge the persistent role of civic groups in caring for the environment and enlivening democratic practice.
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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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.014 |
| 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".