From campaign-style response to positive response: how local actors’ behaviors affect the effectiveness of polycentric water governance
Bibliographic record
Abstract
Resolving complex social-ecological challenges requires many actors to coordinate and collaborate to take concerted governance actions. This is consistent with the concept of polycentricity, which focuses on self-governance. Yet polycentric governance does not always perform well. This study analyzes how polycentric principles can be effectively deployed on the ground by multiple local actors when governance systems exhibit the traits of polycentricity. An attractive case study for this research is the polycentric approach to the water governance of China’s Wanfeng Lake, which gradually transitioned from failure to success since the end of 2018. Combining Institutional Analysis and Development (IAD) and social-ecological system (SES) frameworks, we capture complex multi-level interactions and local actors’ informal behaviors before and after the Wanfeng Lake governance transformation. The research finding shows that the evolution in behavior from campaign-style response to positive response promoted the polycentric governance transformation from failure to success through enhancing the degree of self-governance and improving the coordination mechanisms. This study contributes to a deeper and broader understanding of polycentricity in the following ways: (i) it identifies the barriers and conditions to effective polycentric water governance when there are multiple governance actors; (ii) it displays a polycentric pattern with both formal and informal operations in developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".