Understanding the complex power dynamics that shape collaboration and social learning in multi-stakeholder water governance
Bibliographic record
Abstract
The relationship between power dynamics and decision making in natural resource management is central to explaining governance outcomes. Contemporary catchment governance is increasingly characterized by the interaction of multiple stakeholder groups, which has shifted processes like collaboration and social learning into the focus of water governance research and related fields. Because collaboration and social learning are effective tools for resilience building through, for example, strengthening social capital and network relationships, there is need to better understand how power dynamics influence processes of collaboration and learning and consequential decision making. A three-dimensional power theory was applied to elucidate how instrumental, structural, and discursive power dynamics shape collaboration and social learning in catchment governance, and their effects on governance outcomes. The development process of the Lockyer Valley Catchment Action Plan (Australia) in 2015–2016 was used as a case study. Twenty-five interviews with three diverse stakeholders were conducted and thematically analyzed to extract power evidence from this example of a real-world multi-stakeholder governance process. We identified three main hubs of power, namely: (1) power of facilitation; (2) power of trust; and (3) power of politics. These hubs were characterized by a multitude of strongly interlinked instrumental, structural, and discursive power dynamics. Understanding these hubs of power allow the identification of intervention points to strengthen water governance effectiveness in times of water crisis.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| 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".