The science-policy interface in transboundary water management regimes: the case of Lake Victoria
Bibliographic record
Abstract
The degree that science is integrated into environmental policy processes is dependent on the policymakers' perception of the role and utilization of science in policy development. Using existing literature and interviews with key individuals in North America and Uganda engaged in environmental policy development, this thesis argues that adopting a positivist approach to policy development strengthens the science-policy interface and can result in more effective policies. This interface is examined in the context of transboundary water management, and specifically in East Africa's Lake Victoria management regime. This paper illustrates how the relationship between science and policy is evolving in a difficult ecological, socio-economic and political setting, and how the tensions that exist are attempting to be resolved. It is observed that the science-policy divergence can be exacerbated in transboundary areas. This requires scientists and policymakers to be cognizant of these challenges, and to adopt tools to strengthen the interface for the development of effective transboundary water management regimes.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| 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".