Testing social network metrics as proxies for governance performance: A simulation-based experiment in watershed management
Bibliographic record
Abstract
This study introduces a simulation-based modelling framework to systematically evaluate whether widely used social network analysis (SNA) metrics function as credible proxies for governance performance. I generated 100 synthetic governance networks with covariance structures linking collaboration, equity, resilience, participation, and coordination to structural properties. A suite of analyses, including multiple regression models, permutation tests, partial correlations, and hierarchical clustering, was applied to test the predictive validity of reciprocity, transitivity, Gini degree, k-core, betweenness centrality, clustering coefficient, modularity, and density. Results demonstrate reproducible structural–functional linkages: reciprocity and transitivity robustly predict collaboration, equity is inversely tied to Gini degree, and resilience depends on k-core prominence and betweenness centrality. The modelling workflow, implemented in Python with open scripts and datasets, provides transparent benchmarks for interpreting governance-relevant network metrics. Beyond advancing theory, this framework enhances the diagnostic utility of SNA, supporting more reliable decision-support tools for watershed governance and environmental management. By embedding governance processes into a reproducible, simulation-based workflow, this study extends the reach of ecological informatics beyond biophysical systems to include social structures that shape environmental outcomes. The approach provides transferable benchmarks and open-source resources that strengthen reproducibility, comparability, and integration of governance diagnostics within ecological informatics research.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".