Expanding the boundaries of climate resilient futures: Participatory cross-impact balances in the Red River Basin
Bibliographic record
Abstract
The long-term resilience of river basins around the world – conceptualized as coupled social-ecological systems – is influenced by uncertain drivers of change, including climate change. As actors attempt to make river basins more “resilient”, questions remain about what aspects of these systems are being made more resilient, which conditions they are becoming resilient to, and who the resilience is for. Addressing such questions require more exploratory and reflexive means for anticipating and imagining the future than are currently mainstream in the water sector. In this scenario modelling study, I partnered with the Red River Basin Commission and the International Institute for Sustainable Development to develop a participatory cross-impact balances (CIB) model that characterizes how critical social and ecological uncertainties in the Red River Basin (RRB) influence efforts to build resilience to climate change, and vice versa (to 2050). The RRB is a transboundary basin shared by Minnesota, North Dakota, South Dakota (United States), and Manitoba (Canada). Qualitative analysis of 45 virtual interviews conducted over two rounds with interviewees representing perspectives from diverse geographies (US and Canada), levels of governance (transboundary, federal, state/provincial, municipal, watershed, and Indigenous) and areas of expertise (agriculture, climate, ecology, governance, water management, Indigenous knowledge and governance) generated 15 interacting critical uncertainties, each with multiple possible end-states. These uncertainties ranged from climate change and water quality to Indigenous water rights and agricultural markets. Interactions between uncertainties were discussed with interviewees and influence judgments were coded by the analyst. A sensitivity analysis of the influence judgments in the model to both scientific uncertainty and ambiguity (i.e., multiple frames) revealed divergent assumptions about the system and what constitutes desirable resilient future. Analysis of these results revealed dozens of internally consistent scenarios and 8 scenarios that were robust to divergent model assumptions. After briefly presenting the study results, I will share reflections on the non-standard methodological aspects of my scenario analysis. Namely, I will discuss 1) the opportunities and constraints associated with participatory aspects of the study and 2) the utility of a rigorous sensitivity analysis that helped build legitimacy with study partners and generated useful results in a scenario context rife with uncertainty and ambiguity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".