Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62(10), 1933-1953.

Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words.Journal of the American Society for Information Science and Technology,62(10), 1933-1953.

Scientometrics

圖書資訊學(LIS)為對於記錄下來的資訊(recorded information)和具有文化意義的文物與標本(culturally meaningful artifacts and specimens)有興趣的研究領域(Bates, 2010)，包括的領域有檔案學(archival science)、 書目(bibliography)、文獻與文類理論(document and genre theory)、資訊學(informatics)、資訊系統(information systems)、知識管理(knowledge management)、圖書資訊學(LIS)、博物館研究(museum studies)、記錄管理(records management)和資訊的社會研究(social studies of information)。過去有許多研究嘗試定義與描述圖書資訊學的領域並且確認其中包含的研究主題，這些研究使用的方法相當廣泛，包含Järvelin & Vakkari (1990, 1993)採用內容分析(content analysis)；Åström (2007, 2010)、Moya-Anegón, Herrero-Solana, & Jiménez-Contreras (2006)和 Persson (1994) 針對期刊或期刊文章進行書目計量分析 (bibliometric analysis) ； Moya-Anegón et al., (2006)和White & McCain (1998)針對作者進行書目計量分析 ；Åström (2002)、 Ding, Chowdhury, & Foo (2001) 和 Janssens, Leta, Glänzel, & De Moor (2006)利用從題名、摘要或全文抽取的詞語進行詞語的共現分析(co-word analysis) ；Sugimoto & McCain (2010)則是用索引詞語的三元共現分析(tri-occurrence analysis) ； van den Besselaar & Heimeriks (2006)利用詞語和參考文獻的組合進行分析；以及Sugimoto, Li, Russell, Finlay, & Ding, (2011)和 Sugimoto & McCain (2010)所使用的主題模型分析方法。

上述的這些方法，許多必須依賴於作者對於領域知識的了解，才能了解領域的主題與認知結構(cognitive structure)，例如White & McCain (1998)基於最重要的作家的集群，觀察資訊科學由圍繞在一個微弱中心的許多專業所組成；Åström (2010)則是透過作者與期刊的映射圖說明這個領域的圖書館學(LS)和資訊科學(IS)之間具有差距。除了是認知結構較不直接的指標之外，引用分析另一個的問題是不同的次領域有不同的發表與引用實務。

論文題名包含許多能夠指出該文章內容的詞語(Buxton & Meadows, 1977; Meadows, 1998)。因此，本研究採用的方法是利用期刊論文題名上的重要詞語進行分析。分析的資料來自16種LIS期刊於1988到2007年發表的10344筆論文資料。

選取100個最常出現於題名的詞語。

本研究使用的分析技術包含詞語的相對頻率(relative frequency)並且根據詞語的共現進行叢集，最後並將詞語以及期刊與發表年度等進行多維尺度分析(multidimensional scaling, MDS)，產生視覺化的結果。

詞語的共現分析以及階層式集群分析的結果發現三個主要分類LS(圖書館學)、IS(資訊科學)、SCI-BIB(科學計量學-書目計量學)以及兩個較小的分類資訊尋求行為(information-seeking behavior)和書目指導(bibliographic instruction)。LS可再細分為學術圖書館專業(academic librarianship)、公共圖書館專業(public librarianship) (包含館藏建立)、資訊素養和學校圖書館專業(information literacy and school librarianship, technology)、政策(policy)、全球資訊網(the web)、知識管理(knowledge management)、數位圖書館(digital libraries)、電子商務(e-commerce)、法律(law)以及學術出版(scholarly publishing)等主題。IS則包含資訊檢索(information retrieval)、網路搜尋(web search)、分類目錄(catalogs)以及資料庫(database)等主題。SCI-BIB也有書目計量指標(bibliometric indicators)、作者生產力(author productivity)與引用研究(citation study)等主題。整體的結構如下圖

從詞語的使用可以發現LIS中有某些持續出現的核心詞語，但也有一些詞語的使用在20年間有明顯的變化，這些都是與科技相關的(technologically related)詞語，這個現象符合Saracevic(1999)所宣稱的LIS是個科技驅動的(technology driven)領域。大致上來說，LIS內的改變可以從資料庫(database)，到數位圖書館(digital libraries)，到全球資訊網(the World Wide Web)等詞語使用的移轉上看得出來。

除了科技驅動的特徵外，LIS同時也有很大的範圍在討論資訊尋求行為，這是LS和IS都共同關心的課題。

A number of empirical studies of LIS have been conducted with the aim of describing and defining the field and identifying research areas within it. These studies applied a wide array of approaches: content analysis (Järvelin & Vakkari, 1990, 1993); bibliometric analysis of journals and journal articles (Åström, 2007, 2010; Moya-Anegón, Herrero-Solana, & Jiménez-Contreras, 2006; Persson, 1994); bibliometric analysis of authors (Moya-Anegón et al., 2006,White & McCain, 1998); co-word analysis of both index terms and words extracted from titles, abstracts, and full text (Åström, 2002; Ding, Chowdhury, & Foo, 2001; Janssens, Leta, Glänzel, & De Moor, 2006); tri-occurrence analysis of index terms (Sugimoto & McCain, 2010); analysis of word-reference combinations (van den Besselaar & Heimeriks, 2006); and topic analysis (Sugimoto, Li, Russell, Finlay, & Ding, 2011; Sugimoto & McCain, 2010).

Some notable studies of cognitive structure of LIS have interpreted topics post hoc, by assigning topicality based on knowledge of the author’s domain (e.g., White & McCain, 1998). In White and McCain’s influential visualization of LIS, they concluded that “information science lacks a strong central author, or group of authors, whose work orients the work of others across the board. The field consists of several specialties around a weak center” (p. 343). However, this analysis was based foremost on the clustering of authors, rather than topics. Similarly, Åström (2010) examined the divide between LS and IS components of the field by a bibliometric mapping of authors and journals. Topicality was assigned through expert knowledge of the domains in which these authors wrote and journals published.

Of the various components of textual documents, the titles, and the choice of words in them, are of particular importance. Title words function as “attention triggers” (Bazerman, 1985, 1988). They are devices for capturing interest in the world where information overload is a norm. Title words

have been called “signal-words”1 (Rip & Courtial, 1984) and “macro-actors” or “macro-terms”2 (Callon et al., 1983). Titles of journal articles themselves have undergone a change during the 20th century, becoming more informative, more specific, and containing a larger number of words that indicate article content (Buxton & Meadows, 1977; Meadows, 1998). Leydesdorff (1989) claims that “title words seem to offer a means of making visible the internal cognitive structure” (p. 217) of a discipline. He also claims that “word structure reflects internal intellectual organization in terms

of the codification of word usage in the relevant disciplines” (Leydesdorff, 1989, p. 221). 

Co-word analysis is based on co-occurrence of words (all words, or selected keywords) extracted from titles, abstracts, or text in general, or the index terms assigned by authors or indexers. Co-word analysis is a method that derives “higher level structures from word-occurrence patterns in text” (Chen, 2003, p. 139). Of particular importance in the context of this study is that co-word analysis is “a means to the elucidation of structures of ideas, problems, and so on, represented in appropriate sets of documents” (Whittaker, Courtial, & Law, 1989, p. 473). 

Although co-word analysis has its limitations, (e.g., Leydesdorff, 1997) primarily because of the

change of usage and meaning of words and the lack of context, such analysis has been considered particularly useful in tracking the development of scientific fields over time (Callon et al., 1991; Noyons & van Raan; Rip & Courtial, 1984), which represents another goal of this study.

Although citation analysis is not subject to the same limitation, it is a less direct indicator of cognitive structure. As already mentioned, studies using citations require post hoc assignment of topics. In addition, citation analysis of LIS is less effective in analyzing the cognitive structure of entire fields due to the different publication and citation practices of subfields, thus leaving even large subfields such as LS often invisible.

Selection of journals and articles. Articles from 16 LIS journals were chosen for inclusion in this study. The journals were selected from a ranked list of the most important journals in the field, according to deans and directors of American Library Association (ALA)-accredited, MLS programs in North America (Nisonger & Davis, 2005).

From this journal set, all research and review articles (10,344) published between 1988 and 2007 were included in the analysis.

Identification of the most frequently occurring LIS words and phrases. Word frequency is an important measure in content analysis. This measure is used to identify the most important research topics or concepts in a field by focusing on the most frequently occurring words.

In this study, we base all analyses on the 100 most frequently occurring LIS words or phrases.