• The Korean Journal of Archival Studies
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• no.64
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• pp.157-186
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• 2020

RRecords management has focused largely on public records in south korea with a relatively low progress in the collection and management of private records with the enactment of Public Records Act in 1999. This study set out to categorize the records of the Coalition for Democratic Reunification and the People's Movement(DRPM) and develop them into a group collection. For this study, literature reviews, analysis and reorganization of records content, development of a classification system, and expert evaluation were carried out. The findings of this study will contribute to greater efficiency in the search of records about the DRPM by developing a classification system to recategorize its records of various sources and enable their easy access and utilization in a digital archive. In addition, the basic structure of producer-oriented classification can be suggested as an example of the organization of a group collection.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.1
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• pp.43-59
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• 2022

Electronic public records are classified simultaneously as production, a preservation period is granted, and after a certain period, they are transferred to an archive and preserved. This study intends to find a way to improve the efficiency in classifying transferred records and maintain consistent standards. To this end, the current record classification work process carried out by the National Archives of Korea was analyzed, and problems were identified. As a way to minimize the manual work of record classification by converging the required improvement, the process of identifying outlier candidates based on a list consisting of classified information of the transferred records was proposed and systemized. Furthermore, the proposed outlier discrimination process was applied to the actual records transferred to the National Archives of Korea. The results were standardized and constructed as a training data format that can be used for machine learning in the future.