The politics of adaptive governance: water reform, climate change, and First Nations’ justice in Australia’s Murray-Darling Basin
Bibliographic record
Abstract
Adaptive water governance scholarship aspires to flexible and responsive governance that is inclusive and supports learning and collaboration among a wide range of stakeholders. Much of this scholarship assumes that polycentric arrangements will facilitate these characteristics as different nodes of decision making adapt and respond to challenges within their arena of authority. However, in the case of both adaptive water governance and polycentricity, there are growing questions as to whether the reality matches the theoretical ideal. Drawing on a case study of Australia’s Murray-Darling Basin, we introduce the concept of a polycentric spectrum to distinguish between systems that resist change from those that enable more adaptive transformative change. In our case study, an overarching national agenda of water reform has generated a perpetual cycle of reviews and inquiries into water governance. We examined 34 reviews conducted since 2004, asking whether, how, and to what extent these recommendations are enabling governance adaptation and transformation versus maintaining conventional paradigms. Our analysis revealed problem-solving logics that have dominated water governance for decades to stymie efforts to move toward the more adaptive and transformative forms of governance required to address two key areas of reform: climate change and First Nations’ water justice. Despite an acknowledged need for substantive reforms, inquiry recommendations perpetuate technocratic (for climate change) or administrative rationalist (for First Nations) approaches. We argue that the reform agenda needs to be directed away from governments as the sole agents of change through deliberate and strategic efforts to engage local level and non-state actors who are central to adaptive water governance. This would require debate about reforms to move beyond how water is allocated and optimized to address how power is redistributed in the system. Our analysis questions whether polycentricity alone is sufficient to enable normatively desirable adaptive water governance, suggesting the need for future work to consider whether other organizing concepts, such as water justice might be required.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".