• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.281-306
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• 2023

This study analyzed the legal deposit, acquisition, and donation status from 2020 to 2022, along with the deposit status of e-publications with issued ISBNs. Through this analysis, the study derived improvement measures to strengthen compliance with legal deposit obligations for domestic e-publications. The key findings are as follows: The collection methods were acquisition (57.07%), legal deposit (41.74%), and donation (1.19%). The file formats varied, including e-books (pdf, epub), webtoons (jpg), and audiobooks (mp3). Most e-publications collected were published from 2012 to 2022, with some from 1960 to 2011. Webtoons dominated acquired materials, while legal deposits mainly comprised e-books. Analyzing the status of e-publications with issued ISBNs, e-books (96.2%) were most common, with the literature field receiving the highest number of ISBNs. Most ISBNs were issued during 2020 to 2022. Looking at the top 10 publishers, the low legal deposit rate indicates the need for improvement. To address this, proposed improvement measures include enhancing publishers' awareness of legal deposits, strengthening incentives and sanctions, encouraging voluntary participation through transparent disclosure of the legal deposit status, and improving the accuracy of data in the ISBN issuance and deposit system.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.65-74
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• 2024

Objective : This study assessed ChatGPT, an artificial intelligence system based on a large language model, for its ability to pass the National Korean Occupational Therapy Licensure Examination (NKOTLE). Methods : Using NKOTLE questions from 2018 to 2022, provided by the Korea Health and Medical Personnel Examination Institute, this study employed English prompts to determine the accuracy of ChatGPT in providing correct answers. Two researchers independently conducted the entire process, and the average accuracy of both researchers was used to determine whether ChatGPT passed over the 5-year period. The degree of agreement between ChatGPT answers of the two researchers was assessed. Results : ChatGPT passed the 2020 examination but failed to pass the other 4 years' examination. Specifically, its accuracy in questions related to medical regulations ranged from 25% to 57%, whereas its accuracy in other questions exceeded 60%. ChatGPT exhibited a strong agreement between researchers, except for medical regulation questions, and this agreement was significantly correlated with accuracy. Conclusion : There are still limitations to the application of ChatGPT to answer questions influenced by language or culture. Future studies should explore its potential as an educational tool for students majoring in occupational therapy through optimized prompts and continuous learning from the data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of IKEEE
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• v.27 no.4
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• pp.464-471
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• 2023

In this paper, we propose a method to reduce age distortion in facial expression image generation using StyleGAN Encoder. The facial expression image generation process first creates a face image using StyleGAN Encoder, and changes the expression by applying the learned boundary to the latent vector using SVM. However, when learning the boundary of a smiling expression, age distortion occurs due to changes in facial expression. The smile boundary created in SVM learning for smiling expressions includes wrinkles caused by changes in facial expressions as learning elements, and it is determined that age characteristics were also learned. To solve this problem, the proposed method calculates the correlation coefficient between the smile boundary and the age boundary and uses this to introduce a method of adjusting the age boundary at the smile boundary in proportion to the correlation coefficient. To confirm the effectiveness of the proposed method, the results of an experiment using the FFHQ dataset, a publicly available standard face dataset, and measuring the FID score are as follows. In the smile image, compared to the existing method, the FID score of the smile image generated by the ground truth and the proposed method was improved by about 0.46. In addition, compared to the existing method in the smile image, the FID score of the image generated by StyleGAN Encoder and the smile image generated by the proposed method improved by about 1.031. In non-smile images, compared to the existing method, the FID score of the non-smile image generated by the ground truth and the method proposed in this paper was improved by about 2.25. In addition, compared to the existing method in non-smile images, it was confirmed that the FID score of the image generated by StyleGAN Encoder and the non-smile image generated by the proposed method improved by about 1.908. Meanwhile, as a result of estimating the age of each generated facial expression image and measuring the estimated age and MSE of the image generated with StyleGAN Encoder, compared to the existing method, the proposed method has an average age of about 1.5 in smile images and about 1.63 in non-smile images. Performance was improved, proving the effectiveness of the proposed method.

• Korea Trade Review
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• v.48 no.3
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• pp.43-68
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• 2023

In this study, in relation to "Chapter 3 Support for Priority Purchase of Technology Development Products" of the 「Market Channel Support Act」, this study investigated the positive growth impact of technology development products subject to preferential purchase on small and medium sized enterprises. The data used for empirical verification is for 371 companies that obtained certification for technology development products subject to preferential purchase in 2016 and Data from SMEs were collected from 2017 to 2021, Sales, operating profit, and net profit was identified, and empirical verification. And conducted through statistical analysis to determine whether it had a positive effect on the growth factors of SMEs. In addition, data from 225 technology development product certification companies were collected, and empirical testing was conducted through t-test analysis on the change in growth factors before and after acquiring certification. As a result of statistical analysis, it was found that the total assets, certified sales, operating profit, and net profit, which are the growth factors of a company, are all positively affected according to the type of technology development product certification. However, in the case of authentication types, some authentications showed significant negative results. In addition, significant results were derived that after acquiring certification had a positive effect on growth factors than before acquiring certification. Consistent with this conclusion, I think that it is effective for technology development-based SMEs to enter the public procurement market and utilize the technology development product priority purchase policy for market exploitation and corporate growth. And the government should strengthen the market support policy to create demand so that SMEs can enter the procurement market and actively utilize the preferential purchase system, and come up with an improvement plan so that public institutions can actively utilize the preferential purchase system.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.63-76
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• 2024

Multi-family housing plays a crucial role as a living and experiencing space, and its environment has a direct impact on the well-being and stability of its residents. Therefore, Crime Prevention Design (CPTED) for multi-family housing is of utmost importance. However, crime-related data in China is not disclosed to the public because of its specificity, making it difficult for researchers to conduct further in-depth studies based on accurate crime data. As a result, the establishment and application of CPTED theory in terms of crime prevention is limited and delayed. This study aims to explore three aspects of CPTED in multi-family housing as perceived by home-buying consumers. It investigated consumer perception of the CPTED, the importance of each element and ways to increase awareness of CPTED in multifamily housing in order to effectively improve multifamily crime prevention design principles and further enhance public safety. This study examined the current state and future trends of CPTED in China by analyzing relevant research reports and literature, aiming to gain insights into the crime prevention awareness of Chinese homeowners. In addition, a survey was conducted on Chinese consumers to unravel the importance of CPTED and increase awareness of its various elements in multifamily-family. This study used a Likert scale and SPSS reliability analysis to determine the cognitive status of multi-family CPTED, the importance of each element, and proposed an improvement plan based on the analysis results. As this study was limited by the difficulty of implementation and the lack of validation of its practical effectiveness, it is recommended that future research needs to validate the effectiveness of crime prevention designs and produce more practical results. Furthermore, it is crucial to utilize this study to inform the implementation of security solutions that are tailored to the unique characteristics of each district. Additionally, it is important to offer guidance on how to enhance community safety by increasing residents' awareness of security through education and information dissemination. The author hopes that the representative multi-family CPTED awareness, the importance of each element, and plans for improvement shall be summarized from this study, and provide foundational data for the future development of CPTED based on the Chinese region.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Information Management
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• v.6 no.5
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• pp.131-134
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• 1973

본고(本稿)시리이즈의 제1보(第一報)에서 우리는 물리(物理), 사회과학(社會科學) 및 공학분야(工學分野)의 12,442명(名)의 과학자(科學者)와 기술자(技術者)에 대한 정보교환활동(情報交換活動)의 78례(例)에 있어서 일반과정(一般過程)과 몇 가지 결과(結果)를 기술(記述)한 바 있다. 4년반(年半) 이상(以上)의 기간(其間)($1966{\sim}1971$)에서 수행(遂行)된 이 연구(硏究)는 현재(現在)의 과학지식(科學知識)의 집성체(集成體)로 과학자(科學者)들이 연구(硏究)를 시작(始作)한 때부터 기록상(記錄上)으로 연구결과(硏究結果)가 취합(聚合)될 때까지 각종(各種) 정형(定形), 비정형(非定形) 매체(媒體)를 통한 유통정보(流通情報)의 전파(傳播)와 동화(同化)에 대한 포괄적(包括的)인 도식(圖式)으로 표시(表示)할 수 있도록 설정(設定)하고 또 시행(施行)되었다. 2보(二報), 3보(三報), 4보(四報)에서는 데이터 뱅크에 수집(蒐集) 및 축적(蓄積)된 데이터의 일반적(一般的)인 기술(記述)을 적시(摘示)하였다. (1) 과학(科學)과 기술(技術)의 정보유통(情報流通)에 있어서 국가적(國家的) 회합(會合)의 역할(役割)(Garvey; 4보(報)) 국가적(國家的) 회합(會合)은 투고(投稿)와 이로 인한 잡지중(雜誌中) 게재간(揭載間)의 상대적(相對的)인 오랜 기간(期間)동안 이러한 연구(硏究)가 공개매체(公開媒體)로 인하여 일시적(一時的)이나마 게재여부(揭載如否)의 불명료성(不明瞭性)을 초래(招來)하기 전(前)에 과학연구(科學硏究)의 초기전파(初期傳播)를 위하여 먼저 행한 주요(主要) 사례(事例)와 마지막의 비정형매체(非定形媒體)의 양자(兩者)를 항상 조직화(組織化)하여 주는 전체적(全體的)인 유통과정(流通過程)에 있어서 명확(明確)하고도 중요(重要)한 기능(機能)을 갖는다는 것을 알 수 있었다. (2) 잡지(雜誌)에 게재(揭載)된 정보(情報)의 생산(生産)과 관련(關聯)되는 정보(情報)의 전파과정(傳播過程)(Garvey; 1보(報)). 이 연구(硏究)를 위해서 우리는 정보유통과정(情報流通過程)을 따라 많은 노력(努力)을 하였는데, 여기서 유통과정(流通過程)의 인상적(印象的)인 면목(面目)은 특별(特別)히 연구(硏究)로부터의 정보(情報)는 잡지(雜誌)에 게재(揭載)되기까지 진정으로는 공개적(公開的)이 못된다는 것과 이러한 사실(事實)은 선진연구(先進硏究)가 자주 시대(時代)에 뒤떨어지게 된다는 것을 발견할 수 있었다. 경험(經驗)이 많은 정보(情報)의 수요자(需要者)는 이러한 폐물화(廢物化)에 매우 민감(敏感)하며 자기(自己) 연구(硏究)에 당면한, 진행중(進行中)이거나 최근(最近) 완성(完成)된 연구(硏究)에 대하여 정보(情報)를 얻기 위한 모든 수단(手段)을 발견(發見)코자 하였다. 예를 들어, 이들은 잡지(雜誌)에 보문(報文)을 발표(發表)하기 전(前)에 발생(發生)하는 정보전파과정(情報傳播過程)을 통하여 유루(遺漏)될지도 모르는 정보(情報)를 얻기 위하여 한 잡지(雜誌)나 2차자료(二次資料) 또는 전형적(典型的)으로 이용(利用)되는 다른 잡지류중(雜誌類中)에서 당해정보(當該情報)가 발견(發見)되기를 기다리지 않는다는 것이다. (3) "정보생산 과학자(情報生産 科學者)"에 의한 정보전파(情報傳播)의 계속성(繼續性)(이 연구(硏究) 시리이즈의 결과(結果)는 본고(本稿)의 주내용(主內容)으로 되어 있다.) 1968/1969년(年)부터 1970/1971년(年)의 이년기간(二年期間)동안 보문(報文)을 낸 과학자(科學者)(1968/1969년(年) 잡지중(雜誌中)에 "질이 높은" 보문(報文)을 발표(發表)한)의 약 2/3는 1968/1969의 보문(報文)과 동일(同一)한 대상영역(對象領域)의 연구(硏究)를 계속(繼續) 수행(遂行)하였다. 그래서 우리는 본연구(本硏究)에 오른 대부분(大部分)의 저자(著者)가 정상적(正常的)인 과학(科學), 즉 연구수행중(硏究遂行中) 의문(疑問)에 대한 완전(完全)한 해답(解答)을 얻게 되는 가장 중요(重要)한 추구(追求)로서 Kuhn(제5보(第5報))에 의하여 기술(技術)된 방법(방법)으로 과학(연구)(科學(硏究))을 실행(實行)하였음을 알았다. 최근(最近)에 연구(硏究)를 마치고 그 결과(結果)를 보문(報文)으로서 발표(發表)한 이들 과학자(科學者)들은 다음 단계(段階)로 해야 할 사항(事項)에 대하여 선행(先行)된 동일견해(同一見解)를 가진 다른 연구자(硏究자)들의 연구(硏究)와 대상(對象)에 밀접(密接)하게 관련(關聯)되고 있다. 이 계속성(繼續性)의 효과(效果)에 대한 지표(指標)는 보문(報文)과 동일(同一)한 영역(領域)에서 연구(硏究)를 계속(繼續)한 저자(著者)들의 약 3/4은 선행(先行) 보문(報文)에 기술(技術)된 연구결과(硏究結果)에서 직접적(直接的)으로 새로운 연구(硏究)가 유도(誘導)되었음을 보고(報告)한 사항(事項)에 반영(反映)되어 있다. 그렇지만 우리들의 데이터는 다음 영역(領域)으로 기대(期待)하지 않은 전환(轉換)을 일으킬 수도 있음을 보여주고 있다. 동일(同一) 대상(對象)에서 연구(硏究)를 속행(續行)하였던 저자(著者)들의 1/5 이상(以上)은 뒤에 새로운 영역(領域)으로 연구(硏究)를 전환(轉換)하였고 또한 이 영역(領域)에서 연구(硏究)를 계속(繼續)하였다. 연구영역(硏究領域)의 이러한 변화(變化)는 연구자(硏究者)의 일반(一般) 정보유통(情報流通) 패턴에 크게 변화(變化)를 보이지는 않는다. 즉 새로운 지적(知的) 문제(問題)에 대한 변화(變化)에서 야기(惹起)되는 패턴에 있어서 저자(著者)들은 오래된 문제(問題)의 방법(方法)과 기술(技術)을 새로운 문제(問題)로 맞추려 한다. 과학사(科學史)의 최근(最近) 해석(解釋)(Hanson: 6보(報))에서 예기(豫期)되었던 바와 같이 정상적(正常的)인 과학(科學)의 계속성(繼續性)은 항상 절대적(絶對的)이 아니며 "과학지식(科學知識)"의 첫발자욱은 예전 연구영역(硏究領域)의 대상(對象)에 관계(關係)없이 나타나는 다른 영역(領域)으로 내딛게 될지도 모른다. 우리들의 연구(硏究)에서 저자(著者)의 1/3은 동일(同一) 영역(領域)의 대상(對象)에서 속계적(續繼的)인 연구(硏究)를 수행(遂行)치 않고 새로운 영역(領域)으로 옮아갔다. 우리는 이와 같은 데이터를 (a) 저자(著者)가 각개과학자(各個科學者)의 활동(活動)을 통하여 집중적(集中的)인 과학적(科學的) 노력(努力)을 시험(試驗)할 때 각자(各自)의 연구(硏究)에 대한 많은 양(量)의 계속성(繼續性)이 어떤 진보중(進步中)의 과학분야(科學分野)에서도 나타난다는 것과 (b) 이 계속성(繼續性)은 과학(科學)에 대한 집중적(集中的) 진보(進步)의 필요적(必要的) 특질(特質)이라는 것을 의미한다. 또한 우리는 이 계속성(繼續性)과 관련(關聯)되는 유통문제(流通問題)라는 새로운 대상영역(對象領域)으로 전환(轉換)할 때 연구(硏究)의 각단계(各段階)의 진보(進步)와 새로운 목적(目的)으로 전환시(轉換時) 양자(兩者)가 다 필요(必要)로 하는 각개(各個) 과학자(科學者)의 정보수요(情報需要)를 위한 시간(時間) 소비(消費)라는 것을 탐지(探知)할 수 있다. 이러한 관찰(觀察)은 정보(情報)의 선택제공(選擇提供)시스팀이 현재(現在) 필요(必要)로 하는 정보(情報)의 만족(滿足)을 위하여는 효과적(效果的)으로 매우 융통성(融通性)을 띠어야 한다는 것을 암시(暗示)하는 것이다. 본고(本稿)의 시리이즈에 기술(記述)된 전정보유통(全情報流通) 과정(過程)의 재검토(再檢討) 결과(結果)는 과학자(科學者)들이 항상 그들의 요구(要求)를 조화(調和)시키는 신축성(伸縮性)있는 유통체제(流通體制)를 발전(發展)시켜 왔다는 것을 시사(示唆)해 주고 있다. 이 시스팀은 정보전파(情報傳播) 사항(事項)을 중심(中心)으로 이루어 지며 또한 이 사항(事項)의 대부분(大部分)의 참여자(參與者)는 자기자신(自己自身)이 과학정보(科學情報) 전파자(傳播者)라는 기본적(基本的)인 정보전파체제(情報傳播體制)인 것이다. 그러나 이 과정(過程)의 유통행위(流通行爲)에서 살펴본 바와 같이 우리는 대부분(大部分)의 정보전파자(情報傳播者)가 역시 정보(情報)의 동화자(同化者)-다시 말해서 과학정보(科學情報)의 생산자(生産者)는 정보(情報)의 이용자(利用者)라는 것을 알 수 있다. 이 연구(硏究)에서 전형적(典型的)인 과학자((科學者)는 과학정보(科學情報)의 생산(生産)이나 전파(傳播)의 양자(兩者)에 연속적(連續的)으로 관계(關係)하고 있음을 보았다. 만일(萬一) 연구자(硏究者)가 한 편(編)의 연구(硏究)를 완료(完了)한다면 이 연구자(硏究者)는 다음에 무엇을 할 것이냐 하는 관념(觀念)을 갖게 되고 따라서 "완료(完了)된" 연구(硏究)에 관한 정보(情報)를 이용(利用)하여 동시(同時)에 새로운 일을 시작(始作)하게 된다. 예를 들어, 한 과학자(科學者)가 동일(同一) 영역(領域)의 다른 동료연구자(同僚硏究者)에게 완전(完全)하며 이의(異議)에 방어(防禦)할 수 있는 보고서(報告書)를 제공(提供)할 수 있는 단계(段階)에 도달(到達)하였다면 우리는 이 과학자(科學者)가 정보유통과정(情報流通過程)에서 많은 역할(役割)을 해낼 수 있다는 것을 알 것이다. 즉 이 과학자(科學者)는 다른 과학자(科學者)들에게 최신(最新)의 과학적(科學的) 결과(結果)를 제공(提供)할 때 하나의 과학정보(科學情報) 전파자(傳播者)가 되며, 이 연구(硏究)의 의의(意義)와 타당성(妥當性)에 관한 논평(論評)이나 비평(批評)을 동료(同僚)로부터 구(求)하는 관점(觀點)에서 보면 이 과학자(科學者)는 하나의 정보탐색자(情報探索者)가 된다. 또한 장래(將來)의 이용(利用)을 위하여 증정(贈呈)이나 동화(同化)한 이 정보(情報)로부터 피이드백을 받아 드렸을 때의 범주(範疇)에서 보면 (잡지(雜誌)에 투고(投稿)하기 위하여 원고(原稿)를 작성(作成)하는 경우에 있어서와 같이) 과학자(科學者)는 하나의 정보이용자(情報利用者)가 되고 이러한 모든 가능성(可能性)에서 정보생산자(情報生産者)는 다음 정보생산(情報生産)에 이미 들어가 있다고 볼 수 있다(저자(著者)들의 2/3는 보문(報文)이 게재(揭載)되기 전(前)에 이미 새로운 연구(硏究)를 시작(始作)하였다). 과학자(科學者)가 자기연구(自己硏究)를 마치고 예비보고서(豫備報告書)를 만든 후(後) 자기연구(自己硏究)에 관한 정보(情報)의 전파(傳播)를 계속하게 되는데 이와 관계(關係)되는 일반적(一般的)인 패턴을 보면 소수(少數)의 동료(同僚)그룹에 출석(出席)하는 경우 (예로 지역집담회)(地域集談會))와 대중(大衆) 앞에서 행(行)하는 경우(예로 국가적 회합(國家的 會合)) 등이 있다. 그러는 동안에 다양성(多樣性) 있는 성문보고서(成文報告書)가 이루어진다. 그러나 과학자(科學者)들이 자기연구(自己硏究)를 위한 주정보전파목표(主情報傳播目標)는 과학잡지중(科學雜誌中)에 게재(揭載)되는 보문(報文)이라는 것이 명확(明確)한 사실(事實)인 것이다. 이러한 목표(目標)에 도달(到達)할 때까지의 각(各) 정보전파단계(情報傳播段階)에서 과학자(科學者)들은 목표달성(目標達成)을 위하여 청중(聽衆), 자기동화(自己同化)된 정보(情報) 및 이미 이용(利用)된 정보(情報)로부터 피이드백을 탐색(探索)하게 된다. 우리가 본고(本稿)의 시리이즈중(中)에 표현(表現)하려 했던 바와 같이 이러한 활동(活動)은 조사수임자(調査受任者)의 의견(意見)이 원고(原稿)에 반영(反映)되고 또 그 원고(原稿)가 잡지게재(雜誌揭載)를 위해 수리(受理)될 때까지 계속적(繼續的)으로 정보(情報)를 탐색(探索)하는 과학자(科學者)나 기타(其他)사람들에게 효과적(效果的)이었다. 원고(原稿)가 수리(受理)되면 그 원고(原稿)의 저자(著者)들은 그 보문(報文)의 주내용(主內容)에 대하여 적극적(積極的)인 정보전파자(情報傳播者)로서의 역할(役割)을 종종 중지(中止)하는 일이 있는데 이때에는 저자(著者)들의 역할(役割)이 변화(變化)하는 것을 볼 수 있었다. 즉 이 저자(著者)들은 일시적(一時的)이긴 하나 새로운 일을 착수(着手)하기 위하여 정보(情報)의 동화자(同化者)를 찾게 된다. 또한 전(前)에 행한 일에 대한 의견(意見)이나 비평(批評)이 새로운 일에 영향(影響)을 끼치게 된다. 동시(同時)에 새로운 과학정보생산(科學情報生産) 과정(過程)에 들어가게 되고 현재(現在) 진행중(進行中)이거나 최근(最近) 완료(完了)한 연구(硏究)에 대한 정보(情報)를 항상 찾게 된다. 활발(活潑)한 연구(硏究)를 하는 과학자(科學者)들에게는, 동화자(同化者)로서의 역할(役割)과 전파자(傳播者)로서의 역할(役割)을 분리(分離)시킨다는 것은 실제적(實際的)은 못된다. 즉 후자(後者)를 완성(完成)하기 위해서는 전자(前者)를 이용(利用)하게 된다는 것이다. 과학자(科學者)들은 한 단계(段階)에서 한 전파자(傳播者)로서의 역할(役割)이 뚜렷하나 다른 단계(段階)에서는 정보교환(情報交換)이 기본적(基本的)으로 정보동화(情報同化)에 직결(直結)되고 있는 것이다. 정보전파자(情報傳播者)와 정보동화자간(情報同化者間)의 상호관계(相互關係)(또는 정보생산자(情報生産者)와 정보이용자간(情報利用者間))는 과학(科學)에 있어서 하나의 필수양상(必修樣相)이다. 과학(科學)의 유통구조(流通構造)가 전파자(傳播者)(이용자(利用者)로서의 역할(役割)보다는)의 필요성(必要性)에서 볼 때 복잡(複雜)하고 다이나믹한 시스팀으로 구성(構成)된다는 사실(事實)은 과학(科學)의 발전과정(發展過程)에서 필연적(必然的)으로 나타난다. 이와 같은 사실(事實)은 과학정보(科學情報)의 전파요원(傳播要員)이 국가적 회합(國家的 會合)에서 자기연구(自己硏究)에 대한 정보(情報)의 전파기회(傳播機會)를 거절(拒絶)하고 따라서 전파정보(電波情報)를 판단(判斷)하고 선별(選別)하는 것을 감소(減少)시키며 결과적(結果的)으로 잡지(雜誌)나 단행본(單行本)에서 비평(批評)을 하고 추고(推敲)하는 것이 배제(排除)될 때는 유형적(有形的) 과학(科學)은 급속(急速)히 비과학성(非科學性)을 띠게 된다는 것을 Lysenko의 생애(生涯)에 대한 Medvedev의 기술중(記述中)[7]에 지적(指摘)한 것과 관계(關係)되고 있다.