Research Hotspots of Regional Resilience and the Visualization of Research Frontiers
Bibliographic record
Abstract
As a hot regional research realm, regional resilience provides a new research perspective toward regional sustainable development. At the same time, regional resilience is also an important research paradigm of sustainability science. Based on literature from a core database (Web of Science) launched between 1991 and 2019 on the topic of "regional resilience," this study presents a knowledge graph analysis of regional resilience research. The citation visualization analysis software (CiteSpace5.0) was applied. Keywords co-occurrence network analysis, co-citation clustering analysis, social network co-occurrence analysis, and other analytical methods were adopted. This research drew the following three conclusions: 1) The quantity of regional resilience research shows an upward trend, and it mainly focuses on ecosystem, regional economy, social ecosystem, and social well-being. Five countries—including the U.S., Australia, Britain, Canada, China—and research institutes such as the University of Stockholm, University of Queensland, James Cook University, Chinese Academy of Sciences, U.S. Geological Survey, and University of Cambridge are strongly competitive in terms of research on resilience. 2) The representative works of scholars such as Ron Martin, Carl Folke, Angeler D. G., Craig R. Allen, and Gillian Bristow laid a solid knowledge foundation for regional resilience studies. Furthermore, their collaboration deepened the research on regional resilience. Ron Martin is a significant scholar in the field of the theory of regional resilience evolution, and his research reflects how regional resilience evolves from equilibrium theory to evolution theory. 3) Regional economy, social ecosystem, social well-being, and method exploration are hot topics in regional resilience research. According to keyword classification and citation clustering analysis, the research hotspots of regional resilience are mainly concentrated in the following four categories: 1) Due to global warming and increases in human activities, ecosystem disturbances have increased, and the protection of ecosystem diversity has become a long-term research topic. In addition, biodiversity and ecosystem services and management have become new growth points and strategic development directions of geography. 2) Recently, the world economy has been gloomy; global climate is continuously deteriorating, while regional unrest and frictional issues are increasing. At present, with increasing uncertainties in the global economy, how regions maintain long-term development under external strikes has become the core focus of regional resilience research. Not only should the economic structure be adjusted and optimized, but the stable supply of regional food and energy as well as a stable political and social environment should also be emphasized in regional resilience research. 3) Methodological breakthroughs are key points in regional resilience research. Regional resilience is an interdisciplinary concept that requires comprehensive interdisciplinary research, social-economic scenario analysis, and the construction of multilevel models. Exploration of interdisciplinary and multi-level methods is conducive to promoting the standardization and rationalization of regional resilience research. 4) Empirical research is the trend of regional resilience research. Combining resilience research with specific situations is beneficial in solving scientific problems, providing scientific guidance in relevant policies, and boosting the significance of regional resilience in the process of policymaking. With the deepening of the internationalization process, combining specific problems of certain regions and conducting theoretical and empirical research on regional resilience have become an inevitable path for researchers.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".