Testing social network metrics as proxies for governance performance: A simulation-based experiment in watershed management
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it