Exploring “big picture” scenarios for resilience in social–ecological systems: transdisciplinary cross-impact balances modeling in the Red River Basin
Bibliographic record
Abstract
Climate change is increasing the frequency and the severity of extreme events in river basins around the world. Efforts to build resilience to these impacts are complicated by the social-ecological interactions, cross-scale feedbacks, and diverse actor interests that influence the dynamics of change in social-ecological systems (SESs). In this study, we aimed to explore big-picture scenarios of a river basin under climate change by characterizing future change as emergent from interactions between diverse efforts to build resilience and a complex, cross-scale SES. To do so, we facilitated a transdisciplinary scenario modeling process structured by the cross-impact balances (CIB) method, a semi-quantitative method that applies systems theory to generate internally consistent narrative scenarios from a network of interacting drivers of change. Thus, we also aimed to explore the potential for the CIB method to surface diverse perspectives and drivers of change in SESs. We situated this process in the Red River Basin, a transboundary basin shared by the United States and Canada where significant natural climatic variability is worsened by climate change. The process generated 15 interacting drivers ranging from agricultural markets to ecological integrity, generating eight consistent scenarios that are robust to model uncertainty. The scenario analysis and the debrief workshop reveal important insights, including the transformative changes required to achieve desirable outcomes and the cornerstone role of Indigenous water rights. In sum, our analysis surfaced significant complexities surrounding efforts to build resilience and affirmed the potential for the CIB method to generate unique insights about the trajectory of SESs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-023-01308-1.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".