Scale, evidence, and community participation matter: lessons in effective and legitimate adaptive governance from decision making for Menindee Lakes in Australia’s Murray-Darling Basin
Bibliographic record
Abstract
Rivers and their interdependent human communities form social-ecologically complex systems that reflect basin scale functionally but are often governed by spatially mismatched governance systems. Accounting for this complexity requires flexible adaptive governance systems supported by legitimacy in decision-making processes. Meaningful community dialogue, information exchange, transparency, and scientific rigor are essential to this process. We examined failings in the adaptive governance of the Menindee Lakes system, a major Australian wetland system on the Barka/Darling River of the Murray-Darling Basin. Ecological sustainability of the Menindee Lakes was a casualty of a top-down governance, driven by the New South Wales Government in pursuit of “water savings” for the Murray-Darling Basin, a large scale, federally influenced region. We used quantitative and qualitative methods to analyze long-term social-ecological impacts and stakeholder perceptions of adaptive governance. State and federal government agencies failed basic processes of adaptive governance, ignoring local environmental sustainability in pursuit of basin scale objectives at great cost to governments, communities, humans, and non-humans. This resulted in the development of an ineffective, technocratic solution that lacked community input, leading to a complete loss of support by local communities, including traditional owners. We emphasize the importance of elements of scale in adaptive governance projects, if such projects are going to be effective and legitimate with consequences of coarse commitments to large spatial scale political and environmental objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".