Dimensions of Adaptive Water Governance and Drought in Argentina and Canada
Bibliographic record
Abstract
Climate change in many local and regional scales is expected to include climate hazards and extreme conditions including hailstorms, droughts, floods, hurricanes, hail, tornadoes and storms. Droughts are serious climate hazards threatening water supply for human consumption and also agricultural production and are anticipated to increase in intensity and duration in both Mendoza, Argentina and southern Alberta, Canada. Both Mendoza and Alberta have irrigated agriculture and their rivers are fed primarily by snowmelt and rainfall runoff from mountainous headwaters. Many similarities exist between water law and governance in the Mendoza river basin, Argentina and the Oldman river basin in southern Alberta, Canada. However, many differences also exist. Can these governance systems ensure the continuation of agricultural production in the area into the future given increased development and climate change?Utilizing the institutional design principles of adaptive capacity and water governance, this paper will compare and contrast the water governance institutional structures in the two study areas. Data was obtained from two multi-disciplinary studies of institutional adaptation to climate change studying vulnerability of local agricultural producers and communities to climate change, and the interplay of water governance structures, and adaptive capacities. The water governance systems of both countries show concerns relating to gaps in information and equitable outcomes; in addition there are concerns of a lack of capacity to enable reflexivity. Both systems have been responsive (although there is room for improvement). Through strengthening these identified weaknesses these systems can continue to be resilient into the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".