Risky ground: Seismic hazards and transectional networks in the Pacific northwest
Bibliographic record
Abstract
The Pacific Northwest faces significant seismic hazards from both great subduction earthquakes and more frequent in-slab events within the Juan de Fuca plate system. This paper presents a breakthrough shift in earthquake risk assessment by integrating geological knowledge from the natural sciences with Actor-Network Theory (ANT) and mobilities research from the social sciences to reconceptualize seismic risk through the lens of transectional networks involving human and non-human actors. We examine the translation processes through which seismic monitoring systems, building codes, emergency response protocols, geological formations, and emerging artificial intelligence/machine learning technologies co-constitute earthquake risk in the region. Drawing from recent advances in uncertainty quantification and economic impact assessment methodologies developed for climate litigation, we argue for more sophisticated measurement protocols that can capture the relational dynamics and cascading effects within seismic networks. The historical record of in-slab earthquakes, including the 24-year gap since the last magnitude 6+ event in 2001, illustrates how temporal patterns emerge from complex interactions between geological agencies and human systems. We develop a novel five-phase integrated transectional risk assessment methodology that holistically accounts for both human and non-human vulnerabilities as they emerge from dynamic network relationships across spatial, temporal, and organizational scales. This methodology operationalizes network mapping, translation analysis, transectional vulnerability assessment, integrated uncertainty quantification, and adaptive intervention design to move beyond traditional hazard-exposure-vulnerability frameworks. The transectional perspective reveals opportunities for earthquake risk reduction that go beyond traditional engineering approaches to encompass network reconfigurations, AI-enhanced monitoring systems, innovative financing mechanisms, and enhanced adaptive capacities across human-non-human assemblages. This interdisciplinary approach provides concrete pathways for developing more effective and equitable earthquake risk management strategies that recognize the agency of both geological processes and technological systems in shaping seismic resilience. • Introduces Actor-Network Theory and transectional analysis to earthquake risk • Develops five-phase transectional methodology with quantitative formulations • Integrates climate litigation methods for earthquake risk assessment frameworks • Analyzes Pacific Northwest seismic patterns including 24-year magnitude 6+ gap • Shows vulnerabilities emerge from network configurations not component properties
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".