Futuragua: Fostering Cross-Scale Knowledge to Inform Social-Environmental Decision Processes for Building Drought Resilience in Highly Seasonal Environments
Bibliographic record
Abstract
Improving resilience to drought in complex social-environmental systems (SES) is extraordinarily important, particularly for rural tropical locations where small changes in climate regimes can have dramatic SES impacts. Efforts to build drought resilience must necessarily be planned and implemented within SES governance systems that involve linkages in water and land use administration from local to national levels. These efforts require knowledge and understanding that links climate and weather forecasts to regional and local hydrology, to social-economic and environmental systems, and to governance processes. In order to provide structure for such complex choices and investments, we argue that a focus on structured decision processes that involve linkages among science, technological perspectives, and public values conducted with agencies and stakeholders will provide a crucial framework for comparing and building insight for pursuing alternative courses of action to build drought resilience. This paper focuses on a regional case study in the seasonally-dry northwest region of Costa Rica, in watersheds rated as most threatened in the country in terms of drought. We present the overall framework guiding the transdisciplinary efforts to link scientific and technical understanding to public values, in order to foster civil society actions that lead to improved drought resilience. Initial efforts to characterize hydrological and climate regimes will be reported along with our approach to linking natural science findings, social inventories in terms of perspectives on SES, and the psychology and patterns of reliance on forecast information that provide the basis for characterizing public understanding. The overall linkage of technical and value information is focused on creating and comparing alternative actions that can potentially build resilience in short and long time frames by building decision making processes involving stakeholders, agencies and interested parties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".