Harnessing Community Science to Support Implementation and Success of Nature-Based Solutions
Bibliographic record
Abstract
Community science (CS), a type of community-based participatory research, plays a crucial role in advancing wide-reaching environmental education and awareness by leveraging the collective power of volunteer participants who contribute to research efforts. The low barriers of entry and well-established methods of participatory monitoring have potential to enable community participant involvement in applications of nature-based solutions (NbS). However, a better understanding of the current state of community-based approaches within NbS could improve feasibility for researchers and practitioners to implement community-based approaches in NbS. Based on the current literature, we discern five community science approaches that support NbS: (1) Environmental monitoring to determine baseline conditions; (2) Involvement of participants in NbS development and planning through discussions and workshops (i.e., co-design of NbS); (3) Using existing CS databases to support NbS design and implementation; (4) Determining the impacts and measuring effectiveness of NbS; and (5) Participation in multifunctional activities. While there are various avenues of participation, we find that CS-driven environmental monitoring (i.e., actions that involve observing, measuring, and assessing environmental parameters and conditions over time) emerges as a cornerstone of planning, implementing, and maintaining the success of NbS. As the proliferation of NbS implementation continues, future work to integrate community-based monitoring studies in NbS applications has potential, albeit far from guaranteed, to improve place-based and local societal and ecological outcomes.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".