Bonnevie-Nebelong, E. (2006). Methods for journal evaluation: journal citation identity, journal citation image and internationalization. Scientometrics, 66(2), 411-424.

Bonnevie-Nebelong, E. (2006). Methods for journal evaluation: journal citation identity, journal citation image and internationalization. Scientometrics, 66(2), 411-424.

Scientometrics

本研究以引用分析對Journal of Documentation (J DOC) 進行評估，並且與JASIST和JIS進行比較。所使用的引用分析方法包括三個方面：以引用的參考文獻為主的期刊引用認同(journal citation identity)、以被引用的情形為主的期刊引證形象(journal citation image)和以出版品本身為主的國際化(internationalisation)。

在期刊引用認同方面有兩種指標。第一種指標是引用對被引用者比(citations/citee-ratio)，計算方式是分析範圍內所有參考文獻數除以參考文獻上出現的期刊種類，如果這個數值愈低，表示出現許多不同種類的期刊，也就是使用的期刊具有多元性(diversity)。另一個指標是自我引用(self-citations)，用來測量期刊在科學領域內的獨立性(isolation)，如果自我引用的程度低表示在科學領域內的影響力高。自我引用指標的測量包括引用文獻中來自本身期刊的比例(self-citing)和期刊被引用的情形下來自本身的比例(self-cited)，前者是期刊引用認同的一部份，而後者則屬於期刊引證形象。

期刊引證形象也包含兩種指標。第一種指標是新期刊擴散因素(new journal diffusion factor)，此一指標分析該期刊時間範圍內每一篇論文平均被引用的期刊種類，代表該期刊的想法出口情形(export of ideas)、跨領域性(transdisciplinarity)以及專殊化(specialisation)程度。另一只標示該期刊的共被引期刊，根據共被引情形以及共被引期刊的期刊影響因素(Journal Impact factor)來加以描述。

國際化是測量出版品以及引用期刊論文的作者地區。

各種分析方法與指標整理為Table 1。

首先，引用對被引用者比的結果如Figure 1。另外，1990到2003年的平均引用對被引用者比，JDOC為1.50，JASIST為1.88，JIS則為1.44。較低的引用對被引用者比表示引用的文獻裡重複的期刊較多種，代表這份期刊有較多元的科學基礎(scientific base)。從結果上看來，JDOC比JASIST的科學基礎多元性較高，但較JIS來得低。

JDOC比JASIST和JIS的文章有較高的比例是書評(boo review)，這使得JDOC的參考文獻數較少，因為書評平均只有1.6到2筆參考文獻。

在1990到2003年間，JDOC、JASIST和JIS等三種期刊引用本身的比例都有下降的趨勢，表示這三種期刊愈來愈不孤立，測量期刊被引用的情形，則可發現JDOC與JIS來自期刊本身的比例則較低，表示它們在這個領域的能見度(visibility)較高。另外，JIS在從1979年開始的前十年引用來自期刊本身的比例較高，則說明了這個期刊在當時為在領域邊緣的新期刊。

JDOC的新期刊擴散因素比其他兩種期刊稍大，並且有往上的趨勢。

經常與JDOC共同引用的前十種期刊如Table 3所示。期刊共被引的相似度以Jaccard Similarity測量。

JDOC上論文作者的地區分布如Figure 10。主要的作者來自西歐地區，並逐漸增加。

引用JDOC論文的作者地區分布則如Figure 11。以北美地區的作者引用最多，但西歐地區則逐漸增加。

The Journal Citation Identity is a reference analysis. It is measured by looking into  the referencing style of the publishing authors. What is their combined citations/(journal) citee-ratio? This means that the total number of references in the journal must be calculated, year-by-year or all years taken together. The result of this is divided with the number of different journals present in the set of references. If the set contains many different journals, the ratio will be lower. Consequently a low average signifies a greater diversity in the use of journals among the authors as part of their scientific base, and thus a wider horizon.

Self-citations are part of the Journal Citation Identity as well as the Journal Citation Image, depending on the perspective. ... They are indicators of the style of a journal. Many self-citations among the references may signify isolation of the journal in the scientific domain (high rate). A low rate of self-citations may indicate a high level of influence in the scientific community.

The Journal Citation Image is based on citation analyses of two types: the New Journal Diffusion Factor (N JDF) and journal co-citation analysis.

The New Journal Diffusion Factor was proposed by Frandsen, and is inspired by Rowlands’ diffusion factor. It measures breadth by number of citing journals per published article. N JDF is the average number of different journals that an average article is cited by within a given time window. The result of this tells about the scientific style and about breadth, export of ideas, transdisciplinarity and degree of specialisation of a journal. N JDF is tested for JDOC in a time perspective.

The Journal Citation Image “the White way” means to do a co-citation journal-by-journal analysis and interpret the result in a qualitative manner. It is thus a means to evaluate a journal by the journals co-cited with the journal in question. The co-cited journals are displayed in a list ranked by frequency of co-incidences, the number of citations for each co-cited journal taken into consideration by application of the jaccard calculations. Also the Journal Impact factor (JIF) is used to evaluate the co-cited journals. The co-cited journals then function as image-makers of the journal in question.

Internationalisation is measured by looking into the geographic locations of both publishing and cited authors of the JDOC.

A high citation/citee ratio means that the journal has many recited journals among its references. A low ratio signifies less journal re-citations and thus a greater diversity of journals as part of the scientific base and a wider horizon among authors.

Journal self-citations. Journal self-citations can be analysed from two perspectives, by self-citing rate and by self-cited rate. The first mentioned is part of the citation identity, the second one is part of the self-image, but the two types of self-citations are treated together here for practical reason.

The three journals all show decreasing self-citing rates during the years 1980–2003. This may signify a tendency towards less isolation of the field.

Waltman, L., Yan, E., & van Eck, N. J. (2011). A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science. Scientometrics, 89(1), 301-314.

Waltman, L., Yan, E., & van Eck, N. J. (2011). A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science. Scientometrics, 89(1), 301-314.

在利用引用做為研究成效測量指標的基礎時，一般有兩種方法，一種是根據領域分類系統(field classification scheme)使引用次數正規化，另一為遞迴式的引用加權(recursive citation weighting)。前者認為不同領域有不同的引用密度(每一出版品上引用的平均數目)，因此需要進行調整。在這種方法裡，調整的方式有兩種：一種是根據出版品所指定的領域進行調整，另一種則是從出版品引用的參考文獻數進行調整，這種方式又稱為來源正規化(source normalization)。後者的遞迴式指標則是認為從有影響力的出版品、有聲譽的期刊以及知名作者處來的引用較為有價值。

過去的研究，根據它們是領域分類系統或是來源正規化已是是否為遞迴式的加權機制分為下面的四種：

可以發現目前並沒有根據分類系統正規化同時採用遞迴式機制的方法，因此本研究便式提出一個根據分類系統正規化同時採用遞迴式機制的引用評估方法。

本研究以圖書資訊學為範例，分析這個領域的期刊以及研究機構的引用影響力。在這裡，圖書資訊學領域是以Journal of the American Society for Information Science and Technology (JASIST) 為種子期刊，以共同引用為基礎，搜尋和JASIST有最強關連的期刊，同時這些期刊也必須被歸類在並且在WoS的資訊科學和圖書館學(information science & library science)主題分類下，結果共有47種期刊。連同JASIST在內的48種期刊便做為圖書資訊學領域的代表。並且針對這些期刊利用它們之間的書目耦合(bibliographic coupling)資料以及VOS叢集演算法歸類成三群，包括圖書館學(Library Science)、資訊科學(Information Science)以及科學計量學(Scientometrics)，期刊的題名與它們的歸類情形，如下表所示：

選取圖書資訊學領域48種期刊從2000到2009年發表的類型為Article或Review的文章進行分析，共12202篇。

首先將圖書資訊學全體視為是一個領域。TABLE 4是根據MNCS評估指標的前十種圖書資訊學期刊，將α分別設為1(也就是沒有遞迴式的機制)及20(遞迴的結果收斂)。在α=1的情形，前十種期刊主要為資訊科學及資訊計量學，圖書館學僅占有三種(4, 8與10)。在α=20時，前十種期刊則幾乎是資訊科學及科學計量學，圖書館學僅有排名第9的一種。同樣以MNCS評估指標來評估研究機構時，也可以發現以科學計量學為主要研究項目的機構在α=20時，獲得更好的排名。

當將圖書資訊學分為三個次領域計算時，從TABLE 6的前十種圖書資訊學期刊，可以看到三個次領域的分布已經比較平衡。

scientometrics

Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators).

Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme.

One stream of research focuses on the development of indicators that aim to correct for the fact that the density of citations (i.e., the average number of citations per publication) differs among fields.

One approach is to normalize citation counts for field differences based on a classification scheme that assigns publications to fields (e.g., Braun and Gla¨nzel 1990; Moed et al. 1995; Waltman et al. 2011). The other approach is to normalize citation counts based on the number of references in citing publications or citing journals (e.g., Moed 2010; Zitt and Small 2008). The latter approach, which is sometimes referred to as source normalization (Moed 2010), does not need a field classification scheme.

A second stream of research focuses on the development of recursive indicators, typically inspired by the well-known PageRank algorithm (Brin and Page 1998). ...  The underlying idea is that a citation from an influential publication, a prestigious journal, or a renowned author should be regarded as more valuable than a citation from an insignificant publication, an obscure journal, or an unknown author.

It is sometimes argued that non-recursive indicators measure popularity while recursive indicators measure prestige (e.g., Bollen et al. 2006; Yan and Ding 2010).

To test our recursive MNCS indicator, we use the indicator to study the citation impact of journals and research institutes in the field of library and information science (LIS).

We focus on the period from 2000 to 2009. Our analysis is based on data from the Web of Science database.

We first needed to delineate the LIS field. We used the Journal of the American Society for Information Science and Technology (JASIST) as the ‘seed’ journal for our delineation. We decided to select the 47 journals that, based on co-citation data, are most strongly related with JASIST. Only journals in the Web of Science subject category Information Science & Library Science were considered. JASIST together with the 47 selected journals constituted our delineation of the LIS field.

From the journals within our delineation, we selected all 12,202 publications in the period 2000–2009 that are of the document type ‘article’ or ‘review’.

We first collected bibliographic coupling data for the 48 journals in our analysis. Based on the bibliographic coupling data, we created a clustering of the journals. The VOS clustering technique

(Waltman et al. 2010), available in the VOSviewer software (Van Eck and Waltman 2010),

was used for this purpose. We tried out different numbers of clusters. We found that a solution with three clusters yielded the most satisfactory interpretation in terms of well-known subfields of the LIS field. We therefore decided to use this solution. The three clusters can roughly be interpreted as follows. The largest cluster (27 journals) deals with library science, the smallest cluster (7 journals) deals with scientometrics, and the third cluster (14 journals) deals with general information science topics.

We first consider the case of a single integrated LIS field. The recursive MNCS indicator is said to have converged for a certain α if there is virtually no difference between values of the αth-order MNCS indicator and values of the (α + 1)th-order MNCS indicator. For our data, convergence of the recursive MNCS indicator can be observed for α = 20. In our analysis, our main focus therefore is on comparing the first-order MNCS indicator (i.e., the ordinary non-recursive MNCS indicator) with the 20th-order MNCS indicator.

In the case of the first-order MNCS indicator, the top 10 consists of journals from all three subfields. However, journals from the information science and scientometrics subfields seem to slightly dominate journals from the library science subfield.

Let’s now turn to the top 10 journals according to the 20th-order MNCS indicator. This top 10 provides a much more extreme picture. The top 10 is now almost completely dominated by information science and scientometrics journals. There is only one library science journal left, at rank 9.

The top 10 institutes according to both the first-order MNCS indicator and the 20th-order MNCS indicator are listed in Table 5. Comparing the results of the two MNCS indicators, it is clear that institutes which are mainly active in the scientometrics subfield benefit a lot from the use of a higher-order MNCS indicator.

In Table 6, the top 10 journals according to both the first-order MNCS indicator and the 20th-order MNCS indicator is shown. ...  Comparing Table 6 with Table 4, it can be seen that library science journals now play a much more prominent role, both in the case of the first-order MNCS indicator and in the case of the 20th-order MNCS indicator. As a consequence, the top 10 journals now looks much more balanced for both MNCS indicators.

Åström, F. (2007). Changes in the LIS research front: Time‐sliced cocitation analyses of LIS journal articles, 1990–2004. Journal of the American Society for Information Science and Technology, 58(7), 947-957.

Åström, F. (2007). Changes in the LIS research front: Time‐sliced cocitation analyses of LIS journal articles, 1990–2004. Journal of the American Society for Information Science and Technology, 58(7), 947-957.

scientometrics

本研究利用論文間的共被引分析探討1990到2004年間圖書資訊學(LIS)的研究前沿(research front)的改變，了解這個學科目前的處境與發展趨勢。分析資料為21種LIS期刊。將同時間內具有影響力的共被引文章定義為研究前沿(research fronts)，並且分為三個5年期間，分析領域的改變。研究結果發現LIS由兩個不同研究領域構成的穩定結構：資訊計量學(informetrics)和資訊搜尋與檢索(information seeking and retrieval)，由於分享研究興趣與方法，資訊檢索與資訊計量學有靠近的傾向。而網路為主的研究成為資訊計量學和資訊搜尋與檢索的主要研究則是這個領域的主要變化。

本研究採用的期刊來源為JCR (2003) 的Information Science & Library Science分類下的55種期刊。去除主要是被非LIS期刊引用的期刊以及評論性或商業性期刊後，選擇1990到2004年間有出版的期刊，如下表共21種。

論文的總數為13605筆，從中選取最高被引用的論文，建立共被引次數矩陣。以多維尺度演算法(multidimensional scaling algorithm, MDS)進行處理。
首先是研究基礎(research base)部分，從13605筆論文資料的221586次引用(150145篇參考文獻)中，選取被引用超過50次的文獻，共66筆進行分析。其共被引映射圖如FIG 1.:

與先前研究一致，圖書資訊學在圖形上分為兩個區域，圖形上半部為資訊搜尋與檢索相關論文，下半部則為資訊計量學文獻，此一結果和 Persson (1994) 與 White & McCain (1998)等研究相符合。另外在資訊計量學文獻右邊，還有一群文獻形成網路計量學(webometrics)叢集。網路計量學是利用連結、引用與叢集等資訊計量學方法進行網路本質與特性的分析。

資訊搜尋與檢索從早期的系統導向資訊檢索(systems-oriented information retrieval)發展到使用者-系統互動研究(user-system interaction studies)和資訊行為(information behavior)。

除了網路計量學以外，資訊計量學以書目計量映射(bibliometric mapping)為中心，周圍的部分是書目計量分布(bibliometric distributions)。

為了進一步了解與核對共被引分析的結果，將共被引資料輸入叢集分析。叢集分析所產生的8個叢集符合映射圖的結構，各叢集如TABLE 2。

從TABLE 2各叢集出版年度的中位數，可以將八個叢集分為四個時期：第一個時期圖書資訊學的研究包括實驗性資訊檢索(experimental information retrieval)、書目計量映射以及書目計量分布；第二個時期開始對於資訊檢索的使用者端產生興趣，增加了搜尋過程與認知面向的資訊檢索研究；隨後是在1990年代早期進行的相關性(relevance)研究，同時也傾向於一般的資訊行為；1990年代末期則受到網路科技的影響，開始進行網路以及網路計量學的研究。

接下來的共被引分析，被引用的參考文獻僅限於也在13605篇論文裡的論文，來了解具有影響力的論文，做為研究脈絡(research context)。選取被引用次數超過25次的論文，共65篇。呈現的圖形大致上仍然可明顯的看出分為上半區域的資訊搜尋與檢索和下半區域的資訊計量學。但資訊檢索的研究以認知性資訊搜尋與檢索、相關性和資訊行為為主要，實驗性資訊檢索研究則成為邊緣。

相較於研究基礎，在研究脈絡上可以發現資訊計量學的結果較為分散，包含三個部分：研究合作(research collaboration)、書目計量映射與網路計量學，並且以網路計量學最為主要。

以TABLE 3的叢集結果來看，在研究脈絡中雖然實驗性資訊檢索與書目計量分布消失了，但增加了兒童的資訊行為研究和對於研究合作的資訊計量學分析。雖然這些研究依然存在，但本身並沒有形成叢集，而是歸入其他的叢集中，如IR/Search。

對三個5年的時期進行研究前沿分析，第一個時期1990-1994年，共有3401篇論文，彼此間有1581次引用，39篇論文獲得5次以上的引用。這個時期以ISR為主，特別是使用者觀點的ISR研究；資訊計量學由兩個小叢集組成：一為研究合作，另一聚焦於映射。

第二個時期1995-1999年，包含3318篇論文，彼此間的引用共有2117次，獲得5次以上引用的論文共有52篇。這時期ISR的聚集相當明顯，除了聚焦在資訊科技(information technology)和實驗性資訊檢索(experimental IR)的兩個叢聚外。在一般的資訊計量學之外，另外還有研究成效(research performance)的叢集。

第三個時期2000-2004年，有4147筆論文，彼此間有2926次的引用，62篇論文的引用次數超過7次。在這個時期，可以看出資訊計量學較前面兩個時期緊密連接，主要聚焦在網路計量學，而ISR則較前兩個時期變得較為分散，可分為三個叢集：ISR、兒童的資訊行為(children's information behaviors)以及健康資訊學(health informatics)。

本研究發現LIS有相當穩定的結構，主要為ISR及資訊計量學所構成。另外，從研究基礎上發現，大多為理論或方法學的文獻，但研究脈絡與前沿上的文獻卻以實務性的論文為主。就三個時期的研究來看，1900-1994年以圖書館與資訊服務(library and information service)為主，第二個時期則是線上資料庫與資訊尋求；第三個時期受到WWW影響，主要的研究從群體利用WWW搜尋資訊的方法到發展分析網站影響因素的方法。最後，本研究發現ISR與資訊計量學有愈來愈接近的趨勢，其原因是因為兩者都需要測量文件(或搜尋問題)之間的關係強度，並且也都對將資訊視覺化有興趣，因此彼此引用整合的機會增加。

Based on articles published in 1990–2004 in 21 library and information science (LIS) journals, a set of cocitation analyses was performed to study changes in research fronts over the last 15 years, where LIS is at now, and to discuss where it is heading.

The results show a stable structure of two distinct research fields: informetrics and information seeking and retrieval (ISR). However, experimental retrieval research and user oriented research have merged into one ISR field; and IR and informetrics also show signs of coming closer together, sharing research interests and methodologies, making informetrics research more visible in mainstream LIS research. Furthermore, the focus on the Internet, both in ISR research and in informetrics—where webometrics quickly has become a dominating research area—is an important change.

The nature and intellectual organization of LIS has been thoroughly investigated in analyses describing the general traits of LIS research, as well as mapping how LIS has been organized in different research themes (Persson, 1994; White & Griffith, 1981; White & McCain, 1998).

My approach centers on the following questions. What research topics have dominated LIS during the period 1990–2004? What changes can be observed in the topics addressed over the last 15 years? Can these changes can be used to tell us something about where LIS is heading?

Most definitions of “research fronts” explain them as groups of citing articles being clustered through bibliographic coupling (e.g., Persson, 1994), and their relations to the cited documents clustered by cocitation analysis (Garfield, 1994; Morris et al., 2003; Price, 1965). Although Persson sees the current (citing) articles as the research front and the cited documents as the research base, Garfield, for example, also includes the clusters of cocited core articles into the research front.

In addition, by analyzing the co-occurrence of highly cited documents, we also get an indication on the impact of the articles, thus expanding the definition of research fronts as including influential, as well as current research.

To identify LIS research, and to select journals for the analyses, the Journal Citation Reports: JCR Social Sciences (Thomson ISI, 2003) was used. To defining LIS research, JCR’s Information Science & Library Science classification, covering 55 journals, was used.

To limit the definition, all general LIS journals were identified and the specialized ones were excluded. This was done using the “Citing Journal” field in JCR: If the journal primarily was cited by non-LIS publications, it was excluded from the study.

The analyses were done on a document level, as opposed to an analysis on the author level. Although an author analysis provides more of an overview, the document analysis is more detailed, e.g., by not grouping documents on different topics by the same author.

The result reflects contemporary and influential research within a specific field of research, i.e., the research front.

The research base was based on the 13,605 journal articles published from 1990–2004 and their 221,586 references to 150,145 unique documents. The 66 most-cited documents that received 50 citations or more were selected for further analysis (Figure 1).

The map shows two main areas consistent with the structures found in earlier analyses on LIS (e.g., Persson, 1994; White & McCain, 1998). On the top half of the map, a group of information-seeking and retrieval (ISR) related literature is featured and on the bottom half, a group of informetrics literature. However, on the right side of the informetrics field, a group of webometric studies has formed a cluster. Webometrics is the study of the nature and properties of the World Wide Web, using informetric methodologies such as link, citation, and cluster analyses (Björneborn & Ingwersen, 2001).

In the ISR section of the map, there is a thematic shift from right to left. Systems-oriented information retrieval (IR) literature is on the far right, followed towards the left by user-system interaction studies and information behavior. In comparison to Persson (1994), the “soft” part of the IR-field has increased its impact compared to the “hard” systems-oriented IR research.

Apart from the webometric group on the far right, the informetrics field is centered on bibliometric mapping, surrounded by documents concerning bibliometric distributions.

To enhance the results of the cocitation analysis, a cluster analysis (Persson, 1994) was performed, resulting in eight clusters (Table 2). The clusters support the structures identified in the map, and reveal a division of the soft IR-research: from search- and relevance-focused documents, over cognitive IR and information seeking, to information behavior.

The publication years of the clustered documents shows four generations of research orientations, a trait also visible in the IR part of the map. The first generation of LIS research includes experimental IR, bibliometric distributions, and bibliometric mapping. The second generation of research, with references published from the early 1980s marks the increasing interest in the user side of IR, incorporating the search process and the cognitive perspective into IR and LIS research. This is followed by the relevance studies in the early 1990s; and a contemporary trend to focus on general information behavior. The most recent trend in the LIS research base is studies on World Wide Web and webometrics, dating back to the late 1990s.

The results of the second analysis show influential research areas during the period 1990–2004. It is still the same 13,605 articles providing the material, but only the 18,615 citations to articles present in the set of citing documents are analyzed. Here, as well as in the following time-sliced analysis, the self-citations were removed. Out of the 5024 unique-cited documents, the 65 articles being cited 25 times or more were selected and analyzed (Figure 2).

The general structure of the map is the same: with informetrics on the lower half and ISR on the top half. There are some differences, however. In the top half, a center has developed around “Kuhlthau, 1991” and “Ingwersen, 1996,” focusing on cognitive ISR, relevance, and information behavior, while experimental IR research has become peripheral. Different perspectives on the user-oriented research has dominated the information-seeking and retrieval field; and has together with the wider information behavior field formed a strong research area of different variations on information-seeking research.

At the same time, the informetrics field has become more dispersed, with three clearly defined subfields: research collaboration to the left, bibliometric mapping in the middle, and webometrics on the right side. In comparison with the research base, webometrics has become the dominating research area within the informetrics field.