- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
Vol.40 No.2
Abstract
The purpose of this study is to evaluate and analyze the performance of major reading culture promotion projects implemented in Goyang City, in preparation for the establishment of the 2nd Comprehensive Reading Culture Promotion Plan in Goyang City. To achieve this research objective, this study analyzed various literature and statistical data, identified the reading culture environment in Goyang City, and determined whether major projects implemented according to the 1st Comprehensive Reading Culture Promotion Plan established in 2017 were promoted. In addition, a survey was conducted among librarians in Goyang City by constructing a questionnaire to evaluate the promotion performance of actual relevant projects. Then, by aggregating the data on the reading culture environment in Goyang City, the evaluation results of the reading culture promotion projects obtained from the survey among librarians, and the results of the opinions on the reading promotion projects of the reading surveys of Goyang citizens in the previous relevant study, six major policy directions were proposed to be considered in the establishment of the 2nd Comprehensive Reading Culture Promotion Plan in Goyang City.
Abstract
This study aims to analyze students’ information needs and information-seeking behavior at science schools for gifted through in-depth interviews. The research design was conducted based on previous studies. Through in-depth interviews, this study examined ten students from six out of eight science schools for the gifted in Korea for information needs and overall information-seeking behavior. The results showed the information needs of students at science schools for gifted in the areas of curricular and extracurricular activities as well as the information-seeking behavior in teaching, learning, and research activities, which were the main topics of interest to students based on the ISP model. Based on these results, we identified the preferred information sources in the information-seeking process and discussed the peculiarities and implications of students’ information-seeking behavior. The research is meaningful as it can be used as a basis for further research on the science school for gifted library and as a resource for providing services for students with deep interests and talents in science subject areas.
Abstract
Traditional models for categorizing researcher types have mostly utilized research output metrics. This study proposes a new model that classifies researchers based on the characteristics of research collaboration. The model uses only research collaboration indicators and does not rely on citation data, taking into account that citation impact is related to collaborative research. The model categorizes researchers into four types based on their collaborative research pattern and scope: Sparse & Wide (SW) type, Dense & Wide (DW) type, Dense & Narrow (DN) type, Sparse & Narrow (SN) type. When applied to the quantum metrology field, the proposed model was statistically verified to show differences in citation indicators and co-author network indicators according to the classified researcher types. The proposed researcher type classification model does not require citation information. Therefore, it is expected to be widely used in research management policies and research support services.
Abstract
In this study, a survey was conducted targeting specialized librarians, and the impact on the work area according to changes in the internal and external environment and policy support measures was analyzed. In this study, we tried to derive factors that affect library development and policy suggestions accordingly. As a result of the study, first, it was confirmed that 58.3% of the negative opinions in terms of the importance of library development plans were positive in recognition of the role of library status within individual institutions. Second, in order to increase the status of specialized libraries, it was found that awareness of academic research activities was necessary by recognizing the importance of major functions and roles. Third, among the comprehensive library development plans, the recognition of specialized libraries and operational evaluation was the highest in recognition of the expansion of national public information services to the public. In addition, it was confirmed that among the five-year development strategies, the policy that should be implemented first is the preference for updating the status of specialized libraries and establishing a system for investigation. Fourth, as a result of analyzing effective alternatives and improvement indicators to increase the participation rate in library operation evaluation, the weighting of the “institutional library operation evaluation” item in the evaluation item of public enterprises was the highest at 4.01 on average. Therefore, for the development of specialized libraries, it was recognized as the most urgent task to establish a system that can comprehensively grasp the current status of specialized libraries as well as active academic research and support them.
Abstract
The purpose of this study is to assess the effectiveness of using deep learning language models to extract references automatically and create a reference database for research reports in an efficient manner. Unlike academic journals, research reports present difficulties in automatically extracting references due to variations in formatting across institutions. In this study, we addressed this issue by introducing the task of separating references from non-reference phrases, in addition to the commonly used metadata extraction task for reference extraction. The study employed datasets that included various types of references, such as those from research reports of a particular institution, academic journals, and a combination of academic journal references and non-reference texts. Two deep learning language models, namely RoBERTa+CRF and ChatGPT, were compared to evaluate their performance in automatic extraction. They were used to extract metadata, categorize data types, and separate original text. The research findings showed that the deep learning language models were highly effective, achieving maximum F1-scores of 95.41% for metadata extraction and 98.91% for categorization of data types and separation of the original text. These results provide valuable insights into the use of deep learning language models and different types of datasets for constructing reference databases for research reports including both reference and non-reference texts.
Abstract
As social and political paradigms change, public institution tasks and structures are constantly created, integrated, or abolished. From an effective record management perspective, it is necessary to review whether the previously established record classification schemes reflect these changes and remain relevant to current tasks. However, in most institutions, the restructuring process relies on manual labor and the experiential judgment of practitioners or institutional record managers, making it difficult to reflect changes in a timely manner or comprehensively understand the overall context. To address these issues and improve the efficiency of record management, this study proposes an approach using automation and intelligence technologies to restructure the classification schemes, ensuring records are filed within an appropriate context. Furthermore, the proposed approach was applied to the target institution, its results were used as the basis for interviews with the practitioners to verify the effectiveness and limitations of the approach. It is, aiming to enhance the accuracy and reliability of the restructured record classification schemes and promote the standardization of record management.
Abstract
This study analyzed the structure and utilization of subject headings in the National Library of Korea Subject Headings List (NLSH) based on an analysis of subject headings assigned to 1,218,867 national bibliographies from 2003 to 2022. The findings of the study are as follows: Firstly, among all subject headings in the NLSH, there were 257,103 preferred terms, accounting for 50.2% of the total terms. Foreign language terms constituted 33% (169,466), while non-preferred terms comprised 12% (61,442). Among the preferred terms, 57,312 subject headings were used, accounting for 22.3%. However, it was observed that 54.7% (31,351) of these subject headings were assigned less than 5 times, indicating that only a small number of subject headings were frequently utilized. Secondly, the frequency of relationship indicators appeared in the order of RT, BT, and NT. The NLSH consisted of 12,602 top-level subject headings and 143,704 lowest-level subject headings, with a maximum depth of 17 levels. Thirdly, on average, 1.72 subject headings were assigned per bibliographic record. The number of subject headings assigned and the depth of the hierarchy increased for materials with more specific contents. Recent bibliographic records have been assigned more subject headings and deeper into the hierarchy of the NLSH. It was also found that the number of subject headings assigned per bibliography varied depending on the main class of KDC. Based on the findings, it is recommended to evaluate the coverage of terms in the NLSH, reorganize hierarchical relationships and depth of subject headings, and enhance the development of subdivisions within the NLSH.
Abstract
The purpose of this study is to evaluate the Dublin Core metadata generated by ChatGPT using book covers, title pages, and colophons from a collection of books. To achieve this, we collected book covers, title pages, and colophons from 90 books and inputted them into ChatGPT to generate Dublin Core metadata. The performance was evaluated in terms of completeness and accuracy. The overall results showed a satisfactory level of completeness at 0.87 and accuracy at 0.71. Among the individual elements, Title, Creator, Publisher, Date, Identifier, Rights, and Language exhibited higher performance. Subject and Description elements showed relatively lower performance in terms of completeness and accuracy, but it confirmed the generation capability known as the inherent strength of ChatGPT. On the other hand, books in the sections of social sciences and technology of DDC showed slightly lower accuracy in the Contributor element. This was attributed to ChatGPT’s attribution extraction errors, omissions in the original bibliographic description contents for metadata, and the language composition of the training data used by ChatGPT.
Abstract
With rapidly changing technological implementation of operating systems of businesses, the Ministry of foreign affairs (MOFA) of the Republic of Korea (ROK) has been undergoing digital transformation to its overall operations with the intent to innovate information and knowledge management (IKM) strategies since the mid-2000s. However, assessment as to the effectiveness of implemented IKM has been inadequately analyzed. This study aims to assess the concepts and limitations of the MOFA’s current IKM strategies and the methods it employs to deliver its IKM framework, in light of strengthening the organizational ambidexterity and absorptive capacity, and also fostering organizational innovation through a qualitative study that involves interviews and analysis of reports from MOFA. The MOFA’s IKM possesses dynamic capabilities to adapt to changing digital technologies. However, the institution’s IKM is constrained by limitations associated with the utilization of the IKM system such as a structure that handles confidential documents and a lack of a collaborative system for IKM, and external limitations such as changes in the domestic political situation governing MOFA’s priorities and the hierarchy of government organizations. Consequently, developing the organizational ambidexterity and absorptive capacity was not possible. To develop an IKM framework for organizational innovation, the MOFA must devise a way to minimize the impact of external changes by overcoming internal limitations. To that end, a detailed study on the development of a practically usable IKM system should include establishing a dialogue between job groups and enhancing employee competency in preparation for a changing environment.
Abstract
In the rapidly developing information technology environment, information management organizations need to effectively evaluate their digital maturity and clarify the direction of improvement to effectively respond to rapidly changing environments. This study derived weights for the digital curation maturity model developed by KISTI from the perspective of digital transformation to facilitate effective evaluation and direction setting of information management organizations. Relative importance was derived as a weight in the major and middle categories of the model through the AHP technique. Summarizing the results, when the major categories of the entire model are measured on the basis of 100 points, technology is 27 points, data is 24 points, strategy is 19 points, organization (manpower) is 16 points, and (social) influence is calculated as 14 points. In addition, weights for each subcategory were presented for each major classification based on a perfect score of 100 points. It is expected that a more objective and reasonable evaluation will be possible by applying the weights for each area derived from this study to the digital transformation maturity evaluation model.
Abstract
The purpose of this study is to understand the current status of AI literacy education for users of Korean university libraries and the perception and justification of AI literacy education in university libraries in relation to AI literacy, which is emerging as a key capability in the changing intelligent information society. To this end, this study analyzed the change in the concept of AI literacy and the self-awareness of AI literacy, including generative AI by students who are university library users. As a result of the analysis, positive responses were mainly confirmed in the case of willingness to take AI literacy education and generative AI literacy education in university libraries, and this study suggests that AI literacy education in university essential curriculum is conducted in connection with essential basic education.