How did we get here? The evolution of a polycentric system of groundwater governance
Bibliographic record
Abstract
Polycentric systems are widespread globally and studied extensively, but cross-sectional studies are more prominent than longitudinal studies, and limited attention has been paid to how polycentric systems develop. We present an evolutionary framework to help identify the dynamic factors that shape polycentric system variations and that drive particular trajectories of polycentric formation. Building on prior work, we argue that polycentric institutions for resource management emerge out of spatially delimited conflicts over resource use and the externalities that they entail. Our perspective points to the characteristics and conditions of the resource itself as a starting point that crescively shapes landscape-level patterns of resource use. We illustrate this process through a case study of the evolution of a polycentric system in California’s San Gabriel River Watershed. The study found a relationship between pronounced hydrologic linkages and stronger institutional linkages, suggesting that the physical characteristics of common-pool resources are one driver of subsequent institutional linkages. We also found that the impacts from resource use leads to both conflict and cooperation between basin users that shapes institutional formation and subsequent institutional interactions. This points to user impacts as a second important driver of polycentric formation over time. A better understanding of the evolutionary process of polycentric formation can illuminate opportunities to develop more cooperative relationships that support sustainable groundwater 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".