Understanding the complex power dynamics that shape collaboration and social learning in multi-stakeholder water governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 | 0.000 | 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