How effective is community-based management of freshwater resources? A review
Bibliographic record
Abstract
Despite the existence of numerous research studies on community-based conservation, relatively few focus on the particularities of freshwater ecosystems. Freshwater ecosystems are distinct from terrestrial and marine ecosystems, exhibiting both greater concentrations of biodiversity and elevated threats. In addition, freshwater resources have distinct social, legal, political, and economic characteristics which limit the generalizability of community-based conservation research from other ecological domains. We examine peer-reviewed literature on community-based management of freshwater resources to understand and assess project contexts and outcomes. Our review indicates that studies of freshwater community-based management are limited in number and representativeness. While positive outcomes for both biodiversity and human well-being are commonly reported, limitations due to study design constrain the ability to infer the significance or causality of these effects. Overall, our analysis indicates that there are several gaps in the available research: across geographic regions, freshwater ecosystem types, intervention types, and environmental and human well-being outcome types. Given the importance of freshwater resources to Indigenous Peoples and local communities, our review highlights the critical need to generate evidence across more diverse contexts to achieve greater clarity on whether and how community-based projects can be most effective.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".