Adoption of community monitoring improves common pool resource management across contexts
Bibliographic record
Abstract
Pervasive overuse and degradation of common pool resources (CPRs) is a global concern. To sustainably manage CPRs, effective governance institutions are essential. A large literature has developed to describe the institutional design features employed by communities that successfully manage their CPRs. Yet, these designs remain far from universally adopted. We focus on one prominent institutional design feature, community monitoring, and ask whether nongovernmental organizations or governments can facilitate its adoption and whether adoption of monitoring affects CPR use. To answer these questions, we implemented randomized controlled trials in six countries. The harmonized trials randomly assigned the introduction of community monitoring to 400 communities, with data collection in an additional 347 control communities. Most of the 400 communities adopted regular monitoring practices over the course of a year. In a meta-analysis of the experimental results from the six sites, we find that the community monitoring reduced CPR use and increased user satisfaction and knowledge by modest amounts. Our findings demonstrate that community monitoring can improve CPR management in disparate contexts, even when monitoring is externally initiated rather than homegrown. These findings provide guidance for the design of future programs and policies intended to develop monitoring capabilities in communities. Furthermore, our harmonized, multisite trial provides sustainability science with a new way to study the complexity of socioecological systems and builds generalizable insights about how to improve CPR management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".