Post-crisis risk management: water, community, and adaptation in a South Australian irrigation district
Bibliographic record
Abstract
Farmers in the Langhorne Creek–Angas Bremer basin irrigation district of South Australia have faced a series of hydrosocial crises relating to drought and groundwater depletion and degradation. The crises have been negotiated through concerted community engagement and cooperation. Adaptation responses have included a combination of infrastructural development and changes to the licensing, regulation, and oversight of irrigation governance, easing extraction pressures on the local groundwater catchment. However, new risks have emerged in the wake of, and as a result of, these solutions. One aspect of the solution has been to connect the Angas Bremer basin district more intimately to the much larger continental riverine system, the Murray-Darling basin, which stretches across multiple regional and state jurisdictions. The very success of that scalar response to hydrological risk generates broader systemic risks: to water supply and quality from climate change and upstream extraction; to basin governance; and to community cohesion, engagement, flexibility, and resilience. In a post-crisis period, there is a need to understand the emergent risks from transformational adaptation and guard against complacency to ensure that the hydrosocial qualities of flexibility and resilience that enabled positive responses to the initial crises endure to respond to future crises in water supply and its management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".