Water Governance in Chile and Canada: a Comparison of Adaptive Characteristics
Bibliographic record
Abstract
We compare the structures and adaptive capacities of water governance regimes that respond to water scarcity or drought in the South Saskatchewan River Basin (SSRB) of western Canada and the Elqui River Basin (EB) in Chile. Both regions anticipate climate change that will result in more extreme weather events including increasing droughts. The SSRB and the EB represent two large, regional, dryland water basins with significant irrigated agricultural production but with significantly different governance structures. The Canadian governance situation is characterized as decentralized multilevel governance with assigned water licenses; the Chilean is characterized as centralized governance with privatized water rights. Both countries have action at all levels in relation to water scarcity or drought. This structural comparison is based on studies carried out in each region assessing the adaptive capacity of each region to climate variability in the respective communities and applicable governance institutions through semistructured qualitative interviews. Based on this comparison, conclusions are drawn on the adaptive capacity of the respective water governance regimes based on four dimensions of adaptive governance that include: responsiveness, learning, capacity, including information, leadership, and equity. The result of the assessment allows discussion of the significant differences in terms of ability of distinct governance structures to foster adaptive capacity in the rural sector, highlights the need for a better understanding of the relationship of adaptive governance and good governance, and the need for more conceptual work on the interconnections of the dimensions of adaptive governance.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".