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 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 | 0.110 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".