Governance Institutions and Community Vulnerabilities to Climate-Induced Water Stress – Case studies in Canada and Chile-Draft
Bibliographic record
Abstract
The paper discusses the results of a comparative study of institutional adaptation to climate change and water scarcity in the South Saskatchewan River Basin (SSRB) of western Canada and the Elqui River Basin (ERB) in northern Chile. The study was done in the context of the Institutional Adaptation to Climate Change Project, an interdisciplinary project funded by the Social Science and Humanities Research Council of Canada. The SSRB and the ERB represent two large, regional, dryland water basins with significant irrigated agricultural production but with significantly different governance structures and water management approaches. The Canadian governance situation is characterized as decentralized multi-level governance with assigned water licenses; the Chilean as centralized with privatized water rights. An important determinant in the ability of rural communities and rural households to adapt to current climate variability and future climate change impacts on water resources in environments like the SSRB and the ERB is the institutional setting surrounding water governance and the degree to which this setting facilitates or hinders the community’s adaptive capacity to address climate and other stressors. Based on the definition of vulnerability adopted by the International Panel on Climate Change, the study focuses on governance institutions, and specifically water governance institutions, as one of the main determinants of adaptive capacity of the different rural sectors. In both countries community vulnerability assessments and water governance assessments to present and past climate variability were carried out based on a comprehensive and interdisciplinary approach. The results of these assessments were analyzed in terms of future climate change scenarios and their potential impacts upon the rural sectors of both basins. In these terms, the project provides significant insights about the weakness and strengths of present adaptive capacity in the context of the expected changes in climate variability. As well, the results of the assessments allow comparing and contrasting the differences in terms of capacity of distinct governance structures to foster adaptive capacity in the rural sector. 1
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".