초록

Abstract

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

초록

본 연구에서는 방송자료에 대한 지적 접근점을 제공하기 위한 방편으로, 뉴스 및 시사보도 프로그램의 내용 기술을 위한 패싯 분석 기법의 적용을 시도하였다. 랑가나단의 PMEST 기본 패싯에 기반하여, 보도 장르에 적합한 기본 패싯-‘누가’, ‘무엇을’, ‘어떻게’, ‘어디서’, ‘언제’-을 생성하였으며, 보도 장르의 형식적 구조와 내용적 요소를 반영하여 패싯의 구성요소를 추출하였다. 이를 실제 방영한 시사보도 프로그램을 대상으로 적용해 본 결과, 본 연구에서 제안한 패싯이 보도 장르의 맥락적 요소를 잘 표현해주고 있었으며, 패싯의 적용은 특정 방송내용에 대한 식별을 증진시킬 것으로 기대되었다.

Abstract

This study aims to provide intellectual access to TV content using faceted classification. In order to describe the content of news and current affairs programs, a faceted approach was explored. Based on the Ranganathan’s PMEST formula, the basic facets - ‘who’, ‘what’, ‘how’, ‘where’, ‘when’ - and their sub-facets were created, specifically for describing the news genre. Additionally, the formal structure and the contextual features of the news genre were mainly considered for creating sub-facets. These created facets were applied to a news genre program. The result shows that these suggested facets are useful for representing well the contextual components of the news genre. The application of faceted classification is expected to improve the identification of the specific TV content.