Advances and Prospects in Smart Border Studies Based on a Bibliometric Analysis
Bibliographic record
Abstract
The construction of smart borders has become a key issue in ensuring China's border security, maintaining the country's geopolitical stability, and promoting sustainable development. Against the backdrop of a changing global security landscape, particularly the increasing likelihood of non-traditional security threats, such as cybercrime, terrorism, and environmental risks, the need for a more sophisticated technology-driven approach to border management has become ever-more pressing. By quantitatively analyzing the literature related to smart borders between 2001 and 2022, using CiteSpace software and the Web of Science database as a data source, we aim in this paper to provide a comprehensive review of the development of the field of smart border research, to assess the hotspots and themes of current research, and consider future directions in the field. The results of the study revealed that smart border research is still very much in its infancy, and although there has been a general upward trend in the number of published articles, the volume of relevant research is still comparatively low. Furthermore, the distribution of smart border research groups appears to be relatively decentralized, with only a small number of collaborative initiatives to date, and, as yet, no prominent networked group of authors, with a large proportion of the research being conducted in the United States, the United Kingdom, China, Canada, and Germany. Among the topics addressed in smart border research, border security appears to be the subject of most concern, with immigration management and intelligent surveillance being particular focuses of smart border construction. On the basis of our review, we identified six areas that are the primary focus of current smart border research, namely, the connotation of smart borders, smart border demand, smart border systems, early warning mechanism construction, re-bordering and intelligence, and border strategy and cross-border cooperation. As an emerging research field, despite the preliminary results, smart border studies are still facing problems relating to practical application, such as weak theoretical foundations, an insufficiently developed methodological basis, and conflict between "human defense" and "smart defense," Our findings highlight the need for future studies to focus on establishing a sound theoretical and methodological framework underpinning smart border research, building a smart border control system adapted to China's national conditions, strengthening the precise control and early warning research of border corridors, and examining the modes of multi-subject linkage and humanized governance. Through interdisciplinary integration, drawing on international experience, and promoting the innovation of technology and governance models, the construction of China's smart borders can effectively respond to both traditional and non-traditional security threats, and enhance the precision and efficiency of border management. Having surveyed the current state of smart border research, we propose the following steps to promote the development of China's smart borders. Firstly, smart borders should be comprehensively integrated into the national security strategy to ensure that the system meets China's specific border security needs. A smart border management model with Chinese characteristics should be constructed, emphasizing data sharing, technological interoperability, and coordination and cooperation among all parties. Secondly, with a view toward enhancing border management efficiency, it is recommended that interdisciplinary cooperation be strengthened, international best practices be drawn upon, and technological innovations be promoted. Given the efficient implementation of these measures, China will be able to respond more effectively to traditional and non-traditional security threats, ensure border security and efficient management, and thus maintain stability and sustainable development within an increasingly complex global security environment.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.186 | 0.258 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".