Exploring the Emerging Evolution Trends of Urban Resilience Research by Scientometric Analysis
Bibliographic record
Abstract
Numerous studies in urban resilience have been published in the past decade. However, only a few publications have tracked the evolution trends of urban resilience research, the findings of which can serve as a useful guide for scholars to foresee worth-effort research areas and make the best use of precious time and resources. In order to fill the research gap, this study performed a scientometric analysis on the evolution trends of urban resilience research using a versatile software package-CiteSpace. The scientomentric analysis focuses on distribution of lead authors and their institutions, high frequency categories and keywords, high influential journals, author contribution, and evolutionary trends based on co-author analysis, co-word analysis, co-citation analysis and cluster analysis of documents. This study discoveries that first, the U.S., England, Australia, Canada, China and Sweden are the countries that make the most significant contributions in the advancement of urban resilience research; second, the existing urban resilience research focuses primarily on environmental studies, geography and planning development; third, hot topics of the urban resilience research keep shifting from 1993 to 2016; fourth, the knowledge body of urban resilience research consists of five clusters: resilience exploratory analysis, disaster resilience, urban resilience, urban resilience practice, and social-ecological systems; last, the emerging trends in urban resilience research include defining urban resilience, adaptation model, case studies, analytical methods and urban social-ecological systems, resulting in cutting-edge research areas in urban resilience.
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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.132 | 0.178 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".