Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the adaptive capacity of the institution of water law in two provinces of Canada, Alberta, and Saskatchewan, through the examination of several water conflict case studies in the last decade. By examining outcomes in cases of water shortage, legal mechanisms promoting adaptation can be identified and suggestions made for improving those which potentially increase vulnerability. Design/methodology/approach This paper explores several case studies situated in Western Canada, identified during interviews relating to a broader theme of water governance adaptation as part of the Institutional Adaptation to Climate Change (IACC) Project as well as other case studies carried out in the larger IACC project relating to the institutional adaptation to climate change in Canada and Chile. The outcomes of these case studies are examined in relation to their effect on vulnerability and their inter‐relationship to established principles of water law. Findings This examination provides insight into the actual workings of water law in resolving water conflicts and important modifications in the institution of water law which will increase adaptive capacity. These cases illustrate that legal provisions which facilitate timely engagement of civil society to water shortages in an all inclusive participatory process provides optimal conflict resolution. Originality/value These case studies provide important insights for the development of law and policy which reduces vulnerability and assists people in adapting to climate change in a resilient, effective manner.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".