Sustainability and resilience through connection: the economic metacommunities of the Western USA
Bibliographic record
Abstract
Regional social, environmental, and economic systems form a rich web of connections that both create opportunities and pose risks. Regional economies, characterized by their interconnectedness across jurisdictional boundaries, might be better managed at a transboundary scale because they can leverage a broad resource pool and greater economic diversity compared to a single jurisdiction alone. The technical challenge is to identify which economies are connected and could be managed collectively to better mitigate, absorb, and recover from disruptions. Economic risk management often occurs at the state level, but network approaches can identify groups that interact with one another based on actual commodity flows, capturing important features of the system that are not currently coordinated. One such approach, based on ecological theory, is to identify economic metacommunities. We use theories and methods from metacommunity ecology to identify overarching structures in the Western U.S. trade network. Specifically, we construct commodity flow networks for 25 metro and rural areas, then assess these using the ecological concepts of interaction strength, diversity, clusters, and sources and sinks to identify five economic metacommunities. Based on metacommunity membership, we answer the question: Which regions in the Western USA are interdependent, and are interdependent regions spatially proximate or not? These results are useful in economic development and infrastructure planning for developing redundancy, targeting vulnerable interdependencies, and understanding potential risks from adverse policy exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".