Exploring the Evolution of Seismic Hazard and Risk Assessment Research: A Bibliometric Analysis
Bibliographic record
Abstract
A bibliometric analysis spanning from 2002 to 2022 examines the landscape of seismic hazard and risk assessment research, critical for disaster preparedness in earthquake-prone regions. The study uncovers a substantial increase in related studies, notably surging around 2006. Leading contributors hail from China, the United States, Italy, and the United Kingdom, underlining the global significance of the subject. Common terms in scholarly articles include “seismic hazard”, “seismic risk”, “earthquake”, “vulnerability”, “GIS” (Geographic Information System), and “liquefaction”. While seismic hazards remain the primary focus, a growing interest in risk assessment, particularly for induced phenomena like landslides and liquefaction, is noted. Researchers predominantly assess vulnerability across various structural elements, reflecting a holistic approach to understanding and mitigating the impact of earthquakes on infrastructure and communities. In summary, the bibliometric analysis provides a comprehensive overview of seismic hazard and risk assessment research, highlighting field growth, key research areas, and an increasing focus on risk assessment in response to natural phenomena. The findings offer valuable insights for both academics and practitioners invested in the field’s future development.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.202 | 0.301 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".