Linking stakeholder scenarios and shared socioeconomic pathways for policy making in human-water systems
Bibliographic record
Abstract
Climate change has caused many environmental problems, as well as water and food insecurity, and health and social impacts in many parts of the world, and especially in the world’s vulnerable regions such as developing countries. Studies have demonstrated the impacts of socio-economic and climate changes and how they result in water and environmental problems at global and regional scales. Socio-economic variation and climatic change influence the dynamic interaction of human and water systems, and our ability to address environmental problems at sub-regional scales. From this perspective, the Shared Socio-economic Pathways (SSPs), as a form of alternative development scenarios, were recently introduced to help decision-makers to cope with uncertain futures and improve their policies for mitigation and adaptation to climate change. To take advantage of SSP scenarios for policy guidance at regional and national scales, it is necessary to explore the socio-economic feedbacks and water management policies informed by different sub-regional knowledge sharing through stakeholders’ narratives. In this study, we link SSP scenarios developed with regional stakeholders using a coupled socio-economic and environmental model, in conjunction with stakeholder-generated narrative storylines for a sub-region of Pakistan. The framework allows for linking corresponding scenarios across different uncertainty levels to improve regional scale policy making, while providing knowledge regarding the future of human-water systems under a range of plausible future climate and socio-economic scenarios.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".