From Technological Capability to Disaster Governance in North Korea: An Evidence-Bounded Framework from Scientific Publications
Main Article Content
Authors
Soongsil University
Abstract
This study examines the extent to which scientific publications can support claims about algorithmic disaster governance in an information-limited setting. The empirical corpus consists of two purposively selected 2024 North Korean journal issues—Computer and Software Technology and Geologic and Geographic Science. All 48 scholarly or technical items listed in the two issues were screened, and 14 were retained for structured analysis. Using qualitative document analysis and evidence-bounded coding, the study distinguishes among technological capability, functional compatibility, administrative embedding, and system integration. Three inference rules constrain the escalation of claims: greater functional distance weakens evidentiary support; claims of embedding require connective institutional evidence; and module-level capabilities are non-additive for platform-level claims. The sampled literature documents disaster-risk assessment, remote sensing, terrain processing, deep-neural-network water-depth estimation, routing, sensing, robotics, optimization, and participatory-data workflows. Two qualitative robustness tests clarify the limits of inference. Excluding all transfer-hypothesis cases preserves the modular technical pattern; omitting the principal governance source, however, removes direct support for a full-cycle administrative architecture while leaving the technical-capability portfolio and inference framework intact. The article’s general contribution is therefore an evidence-bounded capability–compatibility–embedding protocol. Modular algorithmic disaster governance is the empirical pattern identified by applying that protocol to this bounded corpus, not a claim that an integrated nationwide system has been deployed.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright remains with the author(s). All articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided that appropriate credit is given to the original author(s) and the source, a link to the license is provided, and any changes are indicated.
How to Cite
References
Akhyar, Akhyar, Mohd Asyraf Zulkifley, Jaesung Lee, Taekyung Song, Jaeho Han, Chanhee Cho, Seunghyun Hyun, Youngdoo Son, and Byung-Woo Hong. 2024. “Deep Artificial Intelligence Applications for Natural Disaster Management Systems: A Methodological Review.” Ecological Indicators 163: 112067. https://doi.org/10.1016/j.ecolind.2024.112067.
Bharosa, Nitesh, JinKyu Lee, and Marijn Janssen. 2010. “Challenges and Obstacles in Sharing and Coordinating Information during Multi-Agency Disaster Response: Propositions from Field Exercises.” Information Systems Frontiers 12 (1): 49–65. https://doi.org/10.1007/s10796-009-9174-z.
Bowen, Glenn A. 2009. “Document Analysis as a Qualitative Research Method.” Qualitative Research Journal 9 (2): 27–40. https://doi.org/10.3316/QRJ0902027.
Choe Kwang Su, and Kim Yong Myong. 2024. “자연재해극복능력평가방법과 공정에 대하여” [Method and process for evaluating natural-disaster coping capacity]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 52.
Comfort, Louise K., Kilkon Ko, and Adam Zagorecki. 2004. “Coordination in Rapidly Evolving Disaster Response Systems: The Role of Information.” American Behavioral Scientist 48 (3): 295–313. https://doi.org/10.1177/0002764204268987.
Goodchild, Michael F. 2007. “Citizens as Sensors: The World of Volunteered Geography.” GeoJournal 69: 211–221. https://doi.org/10.1007/s10708-007-9111-y.
Horita, Flávio E. A., João Porto de Albuquerque, Lívia C. Degrossi, Eduardo M. Mendiondo, and Jó Ueyama. 2015. “Development of a Spatial Decision Support System for Flood Risk Management in Brazil That Combines Volunteered Geographic Information with Wireless Sensor Networks.” Computers & Geosciences 80: 84–94. https://doi.org/10.1016/j.cageo.2015.04.001.
Huh Kum Chol, and Ri Chung Song. 2024. “전국적인 령역에서의 생물다양성중점지역확정” [Identification of biodiversity-priority areas at the national scale]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 40.
Jang Chol Ju, and Pang Hyon Ryong. 2024. “건설지질자름면작도에서 DEM자료와 비저항CT탐사자료의 리용” [Use of DEM and resistivity-CT data in construction-geology section mapping]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 47–48.
Jang Chun Ho, and Choe Un Gyong. 2024. “Research on Iteration Structure Algorithm for Finding Loop Search Method and Optimal Solution in Transportation Problem.” Computer and Software Technology [콤퓨터와 프로그람기술], no. 6: 16–17.
Janssen, Marijn, and George Kuk. 2016. “The Challenges and Limits of Big Data Algorithms in Technocratic Governance.” Government Information Quarterly 33 (3): 371–377. https://doi.org/10.1016/j.giq.2016.08.011.
Kim Jong Ho, and Choe Kwang Myong. 2024. “취약성과 적응능력을 고려한 큰물피해위험성평가” [Flood-damage risk assessment considering vulnerability and adaptive capacity]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 37.
Kim Myong Jin, and Choe Won Il. 2024. “바닥물질이 불균일한 하천류역에서 심층신경망을 리용한 수심 결정방법” [Method for determining water depth in river areas with heterogeneous bottom material using a deep neural network]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 48–49.
Kim, Soonhee, Kim Normann Andersen, and Jungwoo Lee. 2022. “Platform Government in the Era of Smart Technology.” Public Administration Review 82 (2): 362–368. https://doi.org/10.1111/puar.13422.
Krippendorff, Klaus. 2019. Content Analysis: An Introduction to Its Methodology. 4th ed. Thousand Oaks, CA: SAGE Publications. https://doi.org/10.4135/9781071878781.
Kolivand, Pirhossein, Samad Azari, Ahad Bakhtiari, Peyman Namdar, Seyed Mohammad Ayyoubzadeh, Soheila Rajaie, and Maryam Ramezani. 2025. “AI Applications in Disaster Governance with Health Approach: A Scoping Review.” Archives of Public Health 83: 218. https://doi.org/10.1186/s13690-025-01712-2.
Madan, Rohit, and Mona Ashok. 2023. “AI Adoption and Diffusion in Public Administration: A Systematic Literature Review and Future Research Agenda.” Government Information Quarterly 40 (1): 101774. https://doi.org/10.1016/j.giq.2022.101774.
Maragno, Giulia, Luca Tangi, Luca Gastaldi, and Michele Benedetti. 2023. “AI as an Organizational Agent to Nurture: Effectively Introducing Chatbots in Public Entities.” Public Management Review 25 (11): 2135–2165. https://doi.org/10.1080/14719037.2022.2063935.
Mergel, Ines, Noella Edelmann, and Nathalie Haug. 2019. “Defining Digital Transformation: Results from Expert Interviews.” Government Information Quarterly 36 (4): 101385. https://doi.org/10.1016/j.giq.2019.06.002.
Muthukumar, Sanjana, Srishankari Rajesh, Aditi Talpallikar, and Tusar Kanti Mishra. 2026. “A Systematic Review of Artificial Intelligence Frameworks for Holistic Disaster Management.” Discover Artificial Intelligence 6: 308. https://doi.org/10.1007/s44163-026-01020-w.
Ri Hun, and Kim Kwang Chol. 2024. “고분해능위성화상자료를 리용한 도시불투수성표면추출” [Extraction of urban impervious surfaces using high-resolution satellite imagery]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 11–12.
Ri Jong Im, and Ri Yong Dok. 2024. “Auto-encoder Building for Representing Document Vector and User Vector and It’s Application in Document Recommendation.” Computer and Software Technology [콤퓨터와 프로그람기술], no. 6: 6–7.
Ri Kwang Chol, and Jon Un Chol. 2024. “과학연구사업을 추진하는데 이바지하는 《공민과학》” [Citizen science contributing to the advancement of scientific research]. Computer and Software Technology [콤퓨터와 프로그람기술], no. 6: 31–32.
Ri Ryong Jin, and Ji Kyong Il. 2024. “최대경사선분사슬과 단조보간함수에 의한 등고선으로부터의 수자 높이모형생성방법” [Digital elevation model generation from contours using maximum-gradient segment chains and monotonic interpolation]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 35–36.
Ri Su Jin, and Kim Nam Chol. 2024. “Research on the Development of Pipeline Detection Robot Control Device.” Computer and Software Technology [콤퓨터와 프로그람기술], no. 6: 2–3.
Ri Yong Chol, Choe Su Hyang, and Ri Hyo Bom. 2024. “Research on Computer-combined Ultrasonic Flaw Detection System.” Computer and Software Technology [콤퓨터와 프로그람기술], no. 6: 8.
Shin, Andrew. 2025. “Emerging Applications and Implications of Artificial Intelligence in North Korea.” Asian Politics & Policy 17 (3): e70026. https://doi.org/10.1111/aspp.70026.
Sin Chol Guk, and Ju Mun Su. 2024. “가상지구에서 계층도로망타일모형과 최단경로선택방법” [Hierarchical road-network tile model and shortest-path selection method in a virtual earth]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 33–34.
Song Hak Chol, and Ri Hyon Chol. 2024. “록색성장계획작성을 위한 도시록화상태평가방법” [Evaluation method of urban afforestation state for green-growth planning]. Geologic and Geographic Science [지질 및 지리과학], no. 3: 9–10.
Tierney, Kathleen. 2012. “Disaster Governance: Social, Political, and Economic Dimensions.” Annual Review of Environment and Resources 37: 341–363. https://doi.org/10.1146/annurev-environ-020911-095618.
United Nations Office for Disaster Risk Reduction (UNDRR). 2015. Sendai Framework for Disaster Risk Reduction 2015–2030. Geneva: UNDRR. https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030.
van Noordt, Colin, and Gianluca Misuraca. 2022. “Artificial Intelligence for the Public Sector: Results of Landscaping the Use of AI in Government across the European Union.” Government Information Quarterly 39 (3): 101714. https://doi.org/10.1016/j.giq.2022.101714.
Vogl, Thomas M., Cathrine Seidelin, Bharath Ganesh, and Jonathan Bright. 2020. “Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence in UK Local Authorities.” Public Administration Review 80 (6): 946–961. https://doi.org/10.1111/puar.13286.
Wirtz, Bernd W., Jan C. Weyerer, and Carolin Geyer. 2019. “Artificial Intelligence and the Public Sector—Applications and Challenges.” International Journal of Public Administration 42 (7): 596–615. https://doi.org/10.1080/01900692.2018.1498103.
Yeung, Karen. 2018. “Algorithmic Regulation: A Critical Interrogation.” Regulation & Governance 12 (4): 505–523. https://doi.org/10.1111/rego.12158.
Young, Matthew M., Justin B. Bullock, and Jesse D. Lecy. 2019. “Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration.” Perspectives on Public Management and Governance 2 (4): 301–313. https://doi.org/10.1093/ppmgov/gvz014.
Zuiderwijk, Anneke, Yu-Che Chen, and Fadi Salem. 2021. “Implications of the Use of Artificial Intelligence in Public Governance: A Systematic Literature Review and a Research Agenda.” Government Information Quarterly 38 (3): 101577. https://doi.org/10.1016/j.giq.2021.101577.
Article Sidebar
Download
The documentary materials analyzed in this study are the cited 2024 issues of Computer and Software Technology and Geologic and Geographic Science. No newly collected human-subject data were generated.
This work is licensed under a Creative Commons Attribution 4.0 International License.