Facilitating convergence research on water resource management with a collaborative, adaptive, and multi-scale systems thinking framework
Bibliographic record
Abstract
Water resource systems display complex behavior that challenges our ability to identify paths toward improved management. Such behavior can arise from unanticipated feedbacks between social, ecological, and technological components that are conventionally studied and managed in disciplinary silos, often with limited consideration of interactions across scales of space and time. Convergence research driven by deep integration and co-production of knowledge within research teams is needed to better anticipate water resource system behavior and identify new approaches. We developed and applied a new framework—the Collaborative, Adaptive, and Multi-Scale (CAMS) systems thinking framework—to build a convergence research team around the task of characterizing a watershed as a complex system and hypothesize associated water management dynamics. The CAMS framework applies systems thinking methods within a broader integrated approach to engage and synthesize the knowledge and interests of an intellectually diverse research team and model a water resource system across spatial and temporal scales. Our case study of the Santa Fe Watershed in New Mexico reflects challenges and opportunities to manage water in the western United States of America. The specific methods applied within the framework included a six-session workshop on systems thinking, conceptual model development exercises with a longer-term subgroup, a structural analysis of system variables, and classroom-based projects. We discuss the successes, limitations, and potential of each method and how they interacted within the CAMS framework. We found that use of multiple systems thinking methods within the open-ended, iterative design of the framework provided a structure for long-term use that integrates disparate ideas, hypotheses, and findings from water sustainability research. Creating an inclusive environment within the research team was critical to the framework’s successful application and will remain a core consideration for ongoing work aimed at broader participation.
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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 | 0.142 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".