Structural Power Dynamics in Polycentric Water Governance Networks
Bibliographic record
Abstract
Water security is severely threatened by climate change. Building resilient catchments is key to reduce water insecurity, yet power dynamics that generate ineffective governance responses can hinder such resilience building. This research uses an Australian water governance process to study the power dynamics that underpin polycentric governance networks. Combining network theory and Lukes’ structural power concept is a novel methodology tested for its suitability to investigate structural power. We conducted a Social Network Analysis to identify dominant and marginalized stakeholders and validated the results with interview data. Our findings suggest that combining network theory and Lukes’ structural power concept is a useful methodology to identify power dynamics that manifest in polycentric governance structures. This research adds to the methodological foundations of power-related social network research and exemplifies how structural power dynamics, like control over information use, influence the effectiveness of polycentric governance processes and impact desired outcomes like catchment resilience.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 teacher head, 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".