Water, climate change and uncertainty in the Great Lakes and Rio Grande/Bravo Regions
Bibliographic record
Abstract
Abstract Uncertainty is inherent in transboundary water governance, yet climate change is deepening the uncertainties faced by those who manage shared water resources. This paper identifies and assesses uncertainties in the transboundary water governance context by applying an analytical framework which integrates insights from the uncertainty, adaptive governance, and public policy literatures to analyze policy documents in two complex transboundary cases: the Great Lakes and Rio Grande/Bravo basins. Findings from the analysis indicate that: first, deep uncertainties exist in both cases but the two basins face different combinations of, and interactions between, uncertainties; second, the system of scarcity assessed in this analysis (the Rio Grande/Bravo Basin) indicates more conflict-based uncertainties which aggravate natural and technical system uncertainties; and third, the governance system itself is a significant source of uncertainty, or exacerbates existing uncertainties, in both basins. The case studies reveal that governance systems need to focus on different sources, types and levels of uncertainty, and that policy responses need to be designed to move to a ‘monitor-and-adapt’ governance approach to reflect different uncertainties across systems of abundance and scarcity. An analysis of the preparedness of governance systems to respond and adapt to uncertainties is also needed and highly recommended.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".