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Purpose: This paper attempts to reveal the mechanism through which ordinary people initiate information seeking behavior. Although a large number of studies have focused on information seeking behavior, few studies have discussed how this process starts. The present study identified the mechanism by which this process starts based on an in-depth analysis of how ordinary people initiate information seeking behavior. Methods: Seven persons who sought medical information for their family or for themselves participated in the study; each person was interviewed between July and September 2005. The participants outlined their information seeking behavior, explaining why and how they started to seek information. This paper examined the results for three participants who agreed to the publication of the contents of their interviews. Results: The initiation of information seeking behavior was composed of the following three steps: facing the occasion, setting the task, and selecting the information sources. These three steps were linked and organized by the activation of searchers' contexts. First, the activation of searchers' contexts, which is triggered by facing the occasion, prompts the setting of the task. Second, the searchers' contexts are activated again, prompting the selection of information sources. Finally, the selection of actual information sources enables the initiation of information seeking behavior. When people initiate information seeking behavior they must complete all three of these steps. In particular, the activation of searchers' contexts is mandatory for progressing to the three steps. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of text classification using a Bayes coding algorithm, one of the efficient data compression methods. The Bayes coding algorithm gives the Bayes optimal data compression over the tree source model class. When data is compressed by the Bayes coding algorithm, the probability structure of information sources is implicitly estimated from the compressed data. Therefore, we can expect that the implicit estimation of data compression can be utilized for other purposes, especially for the document classification problem. As for the document classification using data compression methods, ZIP format and context tree weighting methods have been proposed. However, these methods do not have Bayes optimal compression and use the compression ratio as a similarity measure between documents for classification. In the Bayes coding algorithm, a weighted mixture tree given by the compression phase can be used for estimated probability structure. Tree source is a class of Markov sources and it is possible to measure the divergence between the tree sources with the same structure. However, the Bayes coding algorithm outputs different tree structures based on the data sequence to be compressed. Since the tree structures derived from documents are different from each other, it is difficult to measure the divergence between them just as it is. This paper proposes a new method to change the structures of weighted mixture trees into the same tree structure to be able to measure the divergence. Using the divergence between trees estimated by documents, the documents can be classified. Moreover, the effectiveness of the proposed method is clarified via a simulation experiment for the document classification with natural data. [ABSTRACT FROM AUTHOR]

Purpose: Subscription based electronic journals which are contained in the "Big Deal" have become predominant in the publication of scholarly journals, but some people predict that open access journals will take its place. This paper quantitatively examines how the status of open access journals has changed in the publication of scholarly journals, and discusses how the status of open access journals will be going in the near future. Methods: Three kinds of surveys were conducted: 1) The primary source was information on 38.803 journals published by 2011 in STM extracted from Ulrichsweb. The source was used to calculate the ratio of open access journals to the total number of journals in 2011. 2) The second data source was journal price lists and the web sites of five major commercial publishers. Both primary and second sources were used to calculate the ratio of new open access journals and new subscription based journals year by year. 3) The web sites of open access mega journals were used as the third source to check their current status. Results: The major findings were: 1) The ratio of open access journals to the total number of journals in STM was 14% in 2011. 2) The ratio of new open access journals to the total number of journals in STM has risen continuously since 2000. The majority of new open access journals have been launched by open access publishers. Among the major commercial publishers, Nature Publishing Group and Springer have begun to increase the number of new open access journals since 2010-2011. 3) Open access mega journals have been launched one after another since 2011. In conclusion. Open access journals are still not the predominant form of publication of scholarly journals, but the business model has become accepted by publishers. [ABSTRACT FROM AUTHOR]

As open-access policies gain acceptance, an increasing number of researchers are contributing their papers to publicly accessible web sites (i.e. self-archiving). Theoretically, these papers are accessible from standard search engines, but they tend to be obscured by other contents on the web. The purpose of this research is to develop a system that can automatically detect academic articles and/or quasi-academic articles on the web. This paper describes experiments that were conducted on the performance of various classifiers and the results are compared in terms of precision, recall, and F-measure. The classifiers use attributes such as terms in PDF files and empirical rules. The results suggest the efficiency of a ranked output system which has several phases to identify academic articles. [ABSTRACT FROM AUTHOR]

This paper examines whether journals covered by the Brandon/Hill List (B/H List journals) could be sorted out by specific evaluation items based on data that can be obtained publicly. Suppose B/H List journals were positioned in the higher rank by specific evaluation items or combinations of items, then B/H List journals could be predicted to some extent using the evaluation items. That is, it would enable us to guess the selection criteria of the B/H List based on those evaluation items. Although it would not be possible to know the real criteria, reasonable equivalents to the selection criteria of the B/H List could be attained using those evaluation items. Impact factor, cited number, citing number, number of articles, cited number/citing number rate, first-publication year, price, price per issue, number of libraries archiving the journal were used as the evaluation items. Data concerning these items were gained from public information resources. Cardiovascular system, internal medicine, and surgery were determined as sample fields from 46 fields of the B/H List 2001 edition. Journals from each field were selected from the journal list of the 2000 edition of Journal Citation Reports on CD-ROM Science edition (JCR). There were 63 cardiovascular journals (7 were B/H list journals), 94 were internal medicine (14 were B/H list journals), and 132 were surgery journals (7 were B/H list journals). In the evaluation items scamined in this study, it became clear that "number of libraries archiving the journal" and "impact factor" can sort out the B/H List journals to some extent. However, B/H List journals were not completely sorted out by the evaluation items used in this research. This result suggests the need for seasoning with the additional factors such as experience, the intuition and other personal knowledge of the librarian, which cannot be expressed as an objective numerical value, to create journal selection criteria. [ABSTRACT FROM AUTHOR]