Wouters, P., & Leydesdorff, L. (1994). Has Price's dream come true: Is scientometrics a hard science?. Scientometrics, 31(2), 193-222.

Wouters, P., & Leydesdorff, L. (1994). Has Price's dream come true: Is scientometrics a hard science?. Scientometrics, 31(2), 193-222.

本研究利用Scientometrics期刊論文以及其參考文獻為研究資料，根據多種資訊判斷科學計量學領域是否已經是硬科學，並分析這個領域的其他特性。本研究所使用的科學計量學資訊有引用文獻的相對年齡(relative age of the cited literature)、論文作者間的關係、論文題名的詞語模式(patterns of words  in the titles of these articles)等。

根據Price的知識增長理論(theory of knowledge growth)，科學家會引用本身領域的文獻，因此，如果有研究前沿(research fronts)存在於這個領域，便會產生立即效應。Price指標(Price index)可以測量立即效應(immediacy effect)，Price指標較大表示引用文獻的相對年齡較低，例如Price(1970)測得生物化學和物理的Price指標值約在60%到70%，社會科學大約在42%附近。Crane(1972)則認為科學會形成作者間彼此緊密相連的社群，因此本研究分析作者間的合著關係和引用的關係，並且利用網絡分析技術探討科學社群的凝聚程度，並且測量作者在網絡的位置連結性以及結構的相似性，根據這些資訊進行叢集，集結彼此間連結性強的作者形成一個叢集，或是形成位置相似的作者叢集。另外，Rip and Courtial (1984)和Leydesdorff (1989a)
指出題名上的詞語可視為是出版品的認知訊息(cognitive message)的指標，詞語在題名上的共現可視為是詞語間關係存在的紀錄，因此本研究也利用網絡分析技術探討詞語的共現網絡。

研究結果發現分析的779筆Scientometrics期刊論文資料，除了前三年快速的增加外，平均每年增加3.5筆，並且這些論文資料共包含12341筆參考文獻，平均每篇論文有15.8筆參考文獻。各項指標都相當穩定。Price指標的平均值為43.0%，若以每年的Price指標的平均值在34.0%到51.4%之間。

以作者資料來看， 779筆論文資料共計由669位不同的作者完成，有接近3/4的作者(488位)僅出現在一筆論文資料上，每位作者平均出現在1.8筆論文資料上，其作者生產力符合Lotka分布，並且大部分(61%)的論文是單一作者，平均每篇論文有1.6位作者。合著作者的論文資料中，大多數的作者都僅和一到兩位同事合作，合著網絡相當破散，但幾個較大的網絡與作者在同一機構任職、參與同一研究計畫或者具有共同的研究興趣有關。

Scientometrics期刊論文的作者引用網絡則呈現高度凝聚的狀態。779筆論文資料中有441筆被其他Scientometrics期刊論文引用，每筆Scientometrics期刊上的論文引用的論文平均有19.4%同樣是Scientometrics期刊的論文。發表超過1篇以上論文的作者共有181位，其中的130位作者有引用其他129位作者的資料，利用作者之間的彼此互相引用關係，發現形成的集團(clique)大多與作者任職機構有關，也有一個集團是成員間曾彼此辯論(debate)而產生。最後，題名上的詞語共現網絡也同樣有高度凝聚的情形。從上面的資訊可以判斷科學計量學領域已經由多種的學科背景在認知與社會性上整合而成，但並沒有發現研究前沿的現象。

In more than one respect, Scientometrics displays the characteristics of a social science journal. Its Price Index amounts to 43.0 percent, and is remarkably stable over time.

The majority of the published items in Scientometrics has been written by a single author. Moreover, the network of co-authorships is highly fragmented: most authors cooperate with no more than one or two colleagues.

Both the citation networks of the authors and the network of title words indicate that the field is nonetheless highly cohesive.

The characteristics of the publications in this journal, and the patterns of the bibliometric relations among them, may therefore indicate the type and extent of the cognitive and social integration of the various disciplinary backgrounds into scientometrics as a field.

The question, in other words, is how "hard" scientometrics is, and how strongly its knowledge is codified. These properties can be measured in terms of:
a) the relative age of the cited literature, the so-called "Price Index",
b) the relations among the authors of articles published in Scientometrics; and
c) the pattern of words in the titles of these articles.

According to Price's theory of knowledge growth (Price 1965), science distinguishes itself from other fields of study by the way scientists refer to their literature (Price 1970). The existence of "research fronts" in science supposedly leads to an "immediacy effect", which can be measured in terms of the so-called "Price Index".

The Price Index is defined as "the proportion of the references that are to the last five years of literature" (Price 1970). Price estimated that this index would vary between 22 and 39 percent if no immediacy effect were present. [1] A field that was all research front and with no general archive might have a Price Index of 75 to 80 percent.

From his analysis of 162 journals, Price (1970) concluded: "Perhaps the most important finding I have to offer is that the hierarchy of Price's Index seems to correspond very well with what we intuit as hard science, soft science, and nonscience as we descend the scale." Biochemistry and physics are at the top, with indexes of 60 to 70 percent, the social sciences cluster around 42 percent, and the humanities fall in the range of 10 to 30 percent.

Science is, on the whole, practised in tightly knit communities in which the authors address one another (Crane 1972).

Co-authorship relations can be considered as indicators of co-operation. [3]

The meaning of citation relations is less clear, given the ongoing citation debate (MacRoberts and MacRoberts 1989; Cozzens 1989; Luukkonen 1990; Leydesdorff and Amsterdamska 1990; Woolgar 1991). But whatever the precise meanings of citations may be, citations can be considered as sociometric data, and the resulting network can accordingly be analyzed (cf. Shrum and Mullins 1988).

We analyzed the extent to which the authors are connected to one another, i.e. the cohesiveness of the network, as well as the pattern displayed by each author in relation to all other authors, i.e. the position of authors in the network.

We also analyzed the similarities among authors in both these dimensions of the matrices, i.e. we clustered strongly connected authors as well as authors in similar positions. Direct as well as indirect linkages between the authors are involved in this analysis.

Strong cliques are sets of authors connected by relations in such a way that all members of the clique are connected to one another, and anyone for whom this holds is included in the clique. The inclusion criterion is less strong for weak cliques, in which all pairs within the clique must have relationships with all other pairs, and anyone with a relation to or from a member of the clique is included.

Strong structural equivalence clusters are sets of authors with completely identical positions in the network (the distance dP between them is zero). Weak structural clusters are sets of authors with a significant similarity in their patterns of relations (the distance dP is small).

As noted, we wished to know whether a structurally codified semantics of scientometrics exists or whether, on the contrary, the articles in Scientometrics use the different terminologies of the various disciplines surrounding scientometrics.

Given the functions of titles of articles, the words in these titles can be considered as indicators of the cognitive message of the publication (Rip and Courtial 1984; Leydesdorff 1989a). The co-occurrence of words in titles can be considered as an indication of the existence or non-existence of relations between these words (Callon et al. 1983).

Since 1978, 779 items have been published in Scientometrics. They contain 12,341 references to the scientific literature. [10]

The number of publications per year in the journal increases in a linear way (Fig. 1). [11] After a steep growth during the first three years, the number increases by 3.5 publications per year.

The number of references per year shows a comparable pattern, although somewhat more irregular. Every publication contains on average 15.8 references (cf. Yitzhala, 1991). Since 1986, this number has become stable at an average of 15 references per publication (Fig. 2).

The publications in Scientometrics were written by 669 different authors. On average, every author published 1.8 times and every paper was written by 1.6 authors. Nearly three-fourth of the authors (488 or 73 percent) published only once in Scientometrics. The distribution of productivity among the authors is a Lotka distribution (Fig. 4).

The average Price Index of Scientometrics is 43.0 percent. ... The Price Index varies between 34.0 and 51.4 percent (Fig. 5). The regression line is not significant. [12] Apparently, the index displays neither rise nor fall since 1978.

Recently, Schubert and Maczelka (1993) concluded from an analysis of Scientomettics in 1980-81 and 1990-91 that the journal has moved slightly from the "soft" (social) towards the "harder" (natural) sciences. They drew this conclusion from the rise of the Price Index from 35 percent to 42 percent between these measurement points. This observation is, however, based on only two measurements. Because of the statistical fluctuations in the value of the Price Index over time, any conclusion can be drawn regarding the development of the Price Index if one restricts oneself to only two measurement points.

In accordance with Price's theory, the number of references to literature of a specific age rises until the cited literature is two years older than the citing literature, and then falls off (Fig. 7). Note that this decline is gradual. Apparently, only a small "immediacy effect" is visible in scientometrics.

A general phenomenon in science is the growth of the number of co-authored scientific articles, relative to the total scientific production (Luukkonen et al. 1992; Abt 1992).

In Scientometrics, however, 61 percent of the articles have been written by a single author. This share is stable over time.

The network of co-authorships is highly fragmented. ... With the exception of three subgroups, most co-authors cooperate with no more than one or two colleagues.

Comparison of the composition of the weak structural equivalence clusters with the relational cliques reveals that two clusters are identical: a group of authors from Leiden (Van Raan et al.) and a group of authors with various institutional affiliations, probably best characterized as the "co-word analysis group". So, these two groups have distinct identities, with respect both to their relations and to their positions in the network.

Some clusters seem constituted by the institutional affiliations of the authors. This holds for the Leiden group and for the authors around ISI (cluster 3). In other cases, nationality appears to be the binding force. This holds for the group in Hungary (cluster 5), the Belgian informetricians (cluster 10) and the Spanish scientometricians (cluster 6). However, cluster 1 can best be characterized by its research program (co-word analysis). Cluster 2 seems to consist of authors from Sussex together with CHI Research Inc. Thus, co-author relations are not only institutionally defined; shared interests and common intellectual goals play a role as well.

To sum up, scientometrics is a fragmentary field of co-authorships. The authors are highly selective in their co-authorship relations with one another. Co-authorships are defined neither exclusively by social nor only by intellectual factors. Both dimensions shape the pattern of co-authorships.

With respect to the number of solitary authors and the large number of isolated small clusters, scientometrics exhibits the pattern of a social science.

Of the 779 articles published in Scientometrics, 411 were subsequently cited one or more times in Scientometrics. The share of references to Scientometrics (as a percentage of all references) has stabilized around an average of 19.4 percent since 1987.

Of the 181 authors in the core set, 130 authors cite one another. So, 51 (or 28.2 percent) of the authors publishing more than one article in Scientometrics from 1978 till 1993 are neither citing nor cited within this group of authors.

The core set of authors in Scientometrics is found to be highly cohesive in terms of their mutual citation relations. All these authors are members of one single weak clique. Moreover, a majority of these authors (88) also belongs to one strong clique (Table 5).

The picture is different if we exclude all indirect relations from the analysis. This "fine structure" of the citation matrix is shown in Table 6, where 13 strong cliques and 6 weak cliques are revealed. Most strong cliques seem to coincide with shared institutional affiliations. The exception is clique 9, which indicates the existence of a debate among the members of this clique.

The most striking feature of the network of title words of articles published in Scientometrics is its cohesiveness. All words cluster together in a single strong component clique (Table 8). If only direct relations are included, all words cluster together in a single weak component clique. This means that all words are either used together in a title or share a common co-word.

Thus, the language of scientometrics is both strongly unified and weakly codified. This strong cohesiveness is a stable characteristic of the titles in Scientornetrics, from the very start of the journal. Perhaps a distinct discourse already existed before the journal was founded. In any case, it constitutes a textual identity of scientometrics as a field, one probably different from the various mother disciplines. Thus a process of de-differentiation seems to have occurred not only in the patterns of citing (and being cited) but also at the cognitive level.

The interpretation of the Price Index is complicated because of these variations within disciplines. If we, nevertheless, take the Price Index preliminary as an indicator of "hardness", scientometrics belongs to the group of relatively hard social sciences. At the same time, it stays unequivocally within the social science range. Taken literally, Price's dream has therefore not come true, since he postulated the emergence of a completely new type of social science with a natural science character. But if we reformulate his goal a posteriori in a more modest way, as the building of a relatively hard social science, it did come true.

The value of the Price Index appears stable over the years. Since a number of other indicators also exhibit stability, this seems to suggest the existence of some scientometric identity. For example, the journal expands at a regular rate, while the percentage of co-authored papers increases only very slowly. The origin of this stability can best be explained by the finding that the community of researchers who have published more than once in Scientometrics acts as a tightly knit network.

In addition to the co-authorships within various institutes, and partly overlapping with this structures, there are national co-authorship relations, like those among the Belgian informetricians, and programmatic co-authorship relations, like those among the users of the French co-word instrument. In general, co-authorship relations are firmly embedded in existing social structures, both at the national and at the community level.

These various strong graphs of co-authors, however, are structurally embedded in the communication structure as indicated by textual indicators. Both in terms of citation relations and in terms of title-words the network is very cohesive, while the structural dimensions of codification are less clear.

In summary, the community of authors publishing in Scientometrics is well integrated, while there are no indications of an exclusive paradigm or a research front.

Schoepflin, U., & Glänzel, W. (2001). Two decades of" Scientometrics". An interdisciplinary field represented by its leading journal. Scientometrics, 50(2), 301-312.

Schoepflin, U., & Glänzel, W. (2001). Two decades of" Scientometrics". An interdisciplinary field represented by its leading journal. Scientometrics, 50(2), 301-312.

本研究以Scientometrics期刊論文的參考文獻資料，對科學計量學領域的特性進行分析。過去的研究裡，Schubert and Maczelka (1993)利用1980-1981和1990-1991兩個時段Scientometrics期刊論文的參考文獻年齡，計算兩個時段的Price指標(Price index)，發現後面時段的Price指標較大於前一個時段的Price指標，因此判斷科學計量學領域正趨向較硬的社會科學。Wouters and Leydesdorff (1994)認為只取樣幾個時段的資料可能不準確，應該觀察資料的連續變化情形，他們的研究指出Price指標在1978到1992年間的變化並不大，所以這個社會科學是較穩定的、並沒有朝向應科學發展的趨勢。Glänzel and Schoepflin (1994)則認為科學計量學是異質性很高的研究領域，這個領域的作者來自不同的學科，擁有相當不同的傳播、引用和發表方式，這種現象使得這個科際整合的領域趨向多元發展。

本研究以1980、1989和1997三個年度的Scientometrics期刊論文的參考文獻做為研究資料，計算以下四個統計資訊：1) 每篇論文的Price指標(the Price index per paper)、2) 參考文獻是連續出版品的百分比(the percentage of references to serials)、3) 參考文獻的平均年齡(the mean reference age)和4) 平均參考文獻數(the mean reference rate)。各年度的論文數與指標如下表所示：


三個年度的期刊Price指標相當穩定，並且所有的指標都反映Scientometriics類似社會科學期刊的模式。此外，表上也呈現每一年度論文的Price指標和連續出版品比率的中位數(median)，這些數據皆明顯大於期刊的數據，表示許多論文比整體期刊呈現的情形來得硬。

本研究並分派每一篇論文到一個類別，這些類別包括：1) 書目計量學理論、數學模型與書目計量學定律的公式化(bibliometric theory, mathematical models and formalisation of bibliometric laws)；2) 案例研究與實務論文(case studies and empirical papers)、3) 方法學論文包含應用(methodological papers including applications)、4) 指標工程與資料呈現(indicators engineering and data presentation)、5) 書目計量學中的社會學取向，科學社會學(sociological approach to bibliometrics, sociology of science)、6) 科學政策、科學管理與廣泛或技術討論(science policy, science management and general or technical discussions)。論文在各類別及各年度的分布如下表所示


可以明顯地發現：1980年度的案例分析和方法學論文所占比例較小，但在後兩個年度則占了論文的大部分；相反的，科學政策從第一個年度後便減少，社會學取向也有相同的情形。本研究推測等Research Evaluation與Social Studies of Science等期刊吸引了相關的論文投稿可能是科學政策與社會學取向方面的論文數量減少的原因。

下表分析各分類論文的參考文獻指標，從這些數據可以看出不同類別的論文在指標上差異很大，特別是科學政策，因此當科學政策在第二和三個年度大幅減少後，期刊整體的指標便有很大的不同。因此，本研究認為科學計量學是異質性很高的領域。



The development of the field of bibliometric and scientometric research is analysed by quantitative methods to answer the following questions:
(1) Is bibliometrics evolving from a soft science field towards rather hard (social) sciences (Schubert-Maczelka hypothesis)?

(2) Can bibliometrics be characterised as a social science field with stable characteristics (Wouters-Leydesdorff hypothesis)?

(3) Is bibliometrics a heterogeneous field, the sub-disciplines of which have their own characteristics? Are these sub-disciplines more and more consolidating, and are predominant sub-disciplines impressing their own characteristics upon the whole field (Glänzel-Schoepflin hypothesis)?

The findings suggest, that the field is in fact heterogeneous, and each sub-discipline has its own characteristics.

Indeed, this journal covers almost the complete spectrum of bibliometric research. It publishes theoretical papers and papers on mathematical models as well as on the research evaluation of special fields and/or selected institutions, on science policy questions as well as articles on social studies of science and general discussions about the field.

While Schubert and Maczelka (1993) found a clear move from softer’ towards ‘harder’ (social) sciences between the analysed time periods 1980-1981 and 1990-1991, respectively, Wouters and Leydesdorff (1994) concluded on the basis of the change of Price’s Index in time that bibliometrics has not become a hard social science field in the observation period 1978-1992.

Glänzel and Schoepflin (1994) stated in their discussion paper that bibliometrics has become a heterogeneous field and sub-disciplines are drifting apart. Consequently, bibliometrics comprises sub-disciplines with distinctly different communication, citation and publication characteristics.

Assuming that bibliometrics is an interdisciplinary field and that authors coming from different fields bring their specific communication behaviour into it, we have classified all papers published in Scientometrics into different categories representing the main field-specific approaches to bibliometrics.

All source articles published in the journal Scientometrics in three sample years, 1980, 1989 and 1997, have been processed. All references cited in articles, notes and letters in the above three publication years were selected. Review articles have not been taken into consideration since the extent and structure of the reference lists of these documents are expected to distinctly differ from those of other research papers. Papers without references have been omitted.

References have been assigned to two categories, reference to serials (S) and reference to non-serials (N). All references have been classified manually.

The following statistics have been calculated.
1. The Price Index per paper. This index is defined as the percentage of references not older than five years in all references of an individual paper. This indicator has been introduced by Moed (1989).
2. The percentage of references to serials. The share of references assigned to category S in all references (N+S) cited by a journal or subfield expressed in percent.
3. The mean references age. The age of references cited in a journal or subfield are summed up and divided by the number of the references. This indicator can be determined also as a conditional mean, that is for both the subset of references in serials and non-serials separately.
4. The mean reference rate. This is the ratio of the number of references cited by a journal and the total number of papers published in the journal including those without references.

The “Price Index” is commonly used as a measure to distinguish between hard science, soft science, technology and non-science (see Price, 1970).

According to the results of an earlier study (Glänzel and Schoepflin, 1999), the percentage of references to serials proved to be a sensitive measure to characterise typical differences in the communication behaviour between the sciences and the social sciences.

The mean reference age also serves as an efficient measure of the “hardness” of science. In the paper by Glänzel and Schoepflin (1999), a comparison of the mean age of references and the Price Index has shown that the age of references is only in part reflected by the Price Index, in particular if the average age of references does not exceed about 15 years.

In addition, we calculated the mean reference rate, that is, the average size” of the reference list of a bibliometric paper published in Scientometrics. Although this cannot be considered a sensitive measure of the “hardness” of science, it reflects nevertheless additional field-specific characteristics (see Glänzel and Schoepflin, 1999).

In the next step, all selected source articles in Scientometrics have been assigned manually to one category out of a scheme of six. ... The classification scheme used for this study is presented in Table 1. The classification permits to group the material in several ways: the categories can be regarded from the viewpoint of core bibliometrics (2, 3, and 4) and background research (1, 5, and 6), but also with respect to theoretical (1, 3, and 5) and applied research (2, 4, and 6).


Table 2 presents the number of papers assigned to each category in 1980, 1989 and 1997.




However, the Price Index shows stability also in our case. Even more, all indicator values reflect patterns typical of social-science journals (c.f. Glänzel and Schoepflin, 1999).

It is worth mentioning here, that the median of paper-based indicators is greater both for the Price Index and the share of serials than the corresponding journal indicators. This phenomenon allows only one possible interpretation: Numerous papers are ‘harder’ than expected on the basis of the overall journal indicators.

There are obviously two dramatic developments: first, there is an impressing and steady growth of Case Studies, from a forth position in 1980 to the predominant first position in 1997. Second, there is a similarly impressive loss of share of articles on Science Policy and Discussions (category 6) from the predominant first position in 1980 to a minor category in 1997. This goes along with a steady loss in material with a sociological approach, too (category 5). On the other hand, there is a certain increase in Methodology (category 3), while Theory and Indicator Engineering remain minor classes.



If we now take a look on Core Bibliometrics as defined by categories 2, 3, and 4, it becomes obvious, that this group is practically reduced to Case Studies and Methodology, and by far dominating the total output of research as represented by the journal Scientometrtics.

The group characterised as Background Research (categories 1, 5, and 6) has continuously lost ground since 1980. Moreover, theoretical research in bibliometrics seems to be mainly a matter of methodology.

Following the differentiation of the field, journals like Social Studies of Science or the newly founded Research Evaluation publish a considerable share of bibliometric research in categories 5 and 6. But also journals in information science (e.g. JASIS, Information Processing & Management, or Journal of Information Science to name just a few Anglo-Saxon titles) attract bibliometric research articles.

On the detriment of a larger scope, Scientometrics has clearly become the forum for Case Studies and Methodology-oriented contributions.

The above-mentioned deviating patterns in 1980 can be at least in part explained by the great share of papers in category 6 and their low share of serials and relatively low age of the references. Indeed, the indicator values of the six categories give evidence of specific characteristics of the corresponding sub-disciplines.



The above trends and figures tell unambiguously against an evolution of bibliometrics towards a discipline of ‘hard’ social science (Schubert-Maczelka hypothesis). On the other hand, we cannot speak of stable characteristics, either (Wouters-Leydesdorff hypothesis). The indicators allow only an interpretation in the sense of the third hypothesis. The field is indeed heterogeneous, and each sub-discipline has its own characteristics. This may, of course, be at least in part caused by the deviating field-specific communication behaviour of the authors who bring traditional organisation schemes from their own fields into bibliometrics.

Chen, C., McCain, K., White, H., & Lin, X. (2002). Mapping Scientometrics (1981–2001). Proceedings of the American Society for Information Science and Technology, 39(1), 25-34.

Chen, C., McCain, K., White, H., & Lin, X. (2002). Mapping Scientometrics (1981–2001). Proceedings of the American Society for Information Science and Technology, 39(1), 25-34.

科學映射圖(science mapping)是整合資訊視覺化(information visualization)和科學計量學(scientometrics)的研究，藉由圖形呈現揭露科學文獻的結構與相關的專業(specialties)，科學映射圖的最基礎技術為詞語的共現分析和共被引分析，分別提供獨特的科學研究前沿結構洞察力，Braam, Moed, & Raan (1991a, 1991b)的研究發現結合這兩種技術能夠讓出版品的認知內容(cognitive content of publications)產生更為清楚的圖像。

科學計量學是測量科學或技術進展的研究 (Garfield, 1979b)。傳統上科學計量學有相當強烈的應用導向，針對科學或技術的輸入與輸出發展出許多測量方法與指標，許多知識工作者以這些測量方法與指標為工具應用於各種不同的研究：例如可以針對國家、地區和研究機構的研發能力進行政策與計畫的評估，或是對於研究領域的知識結構進行領域分析。van Raan (1997)和Persson (2000)都是以Scientometrics期刊論文做為研究資料的研究。van Raan (1997)分析科學計量學的最佳狀態(the state of the art)以及對它的應用導向傳統進行描述，van Raan建議科學計量學需要和知識發現(knowledge discovery)與資料探勘(data mining)整合來獲得明顯的效益。Persson (2000)以1978到1999年，44卷，1062篇論文資料進行分析，找出最常被引用的出版品，並且產生期刊共被引、國家間的直接引用連結、作者間的共被引以及直接引用等圖形，表現各種不同的結構。

本研究以1981到2001年間的Scientometrics期刊論文為研究資料，選擇被引用次數達五次以上的參考文獻，共計403筆文獻，根據這些文獻的共被引資訊，繪製網路圖做為科學映射圖的基本圖形，並以論文的引用速率產生動畫的效果。本研究首先以文獻的共被引次數計算Pearson 相關係數(Pearson's correlation coefficients)產生共被引矩陣(co-citation matrix)。並且利用主成分分析(principal component analysis)對共被引矩陣進行因素分析，以分析出的因素代表領域的專業。同時也利用共被引矩陣產生網路圖，經過尋徑網路縮放(pathfinder network scaling)保留較重要的共被引連結，以簡化圖形的複雜性。最後以VRML(virtual reality modeling language)呈現圖形，並且以動畫呈現文獻的被引用率增長情形。本研究共計找出25個因素，較大的三個因素所對應的專業分別命名為科學研究中的引用(citations in science studies)、全球與國家的科學表現(world and national science performance)、研究產出的評估(evaluation research outputs)。

The design of the visualization model adapts a virtual landscape metaphor with document cocitation networks as the base map and annual citation rates as the thematic overlay. The growth of citation rates is presented through an animation sequence of the landscape model.

Science mapping aims to reveal structures of scientific literature and underlying specialties using graphical representations. ... Co-word analysis (Callon, Law, & Rip, 1986) and co-citation analysis (Small, 1973) are among the most fundamental techniques for science mapping. ... Each offers a unique perspective on the structure of scientific frontiers. Researchers have found that a combination of co-word and co-citation analysis could lead to a clearer picture of the cognitive content of publications (Braam, Moed, & Raan, 1991a, 1991b).

As an integral part of our long-term research, our investigation emphasizes an interdisciplinary synergy that may involve fields of study such as information visualization and scientometrics.

Can we provide domain analysts, science performance evaluators, researchers, students, and other knowledge workers something tangible and meaningful that they can readily incorporate it into their work process? Can we improve the way we learn about a new subject matter, the way we explore a knowledge domain, and the way we trace the history and evolution of a specialty? And ultimately, can we augment our ability to judge the significance of scientific work more efficiently and more accurately?

The present study is based on articles published in Scientometrics between 1981 and 2001, drawn from the Web of Science.

Scientometrics is “the study of the measurement of scientific and technological progress” (Garfield, 1979b). Its origin is in the quantitative study of science policy research, or the science of science, which focuses on a wide variety of quantitative measurements, or indicators, of science at large.

Input indicators include the amount of research grants awarded to institutions and the number of people receiving scientific degrees; output indicators include the number of scientific articles published, the number of citations to each article, and the number of patents granted.

Science policy and program evaluation studies have used such indicators to measure the scientific strength of various countries, regions, or research institutions.

Domain analysts have used such indicators to describe the intellectual structure of a knowledge domain.

Scientometric research has a strong application-oriented tradition (Garfield, 1979b; Raan, 1997).

Garfield (Garfield, 1979b) identified several publications appeared in the 1970s and contributed to the development of scientometrics, namely, the first Science Indicators published by the National Science Board in 1972 (Board, 1977), the Evaluative Bibliometrics: The Use of Publication and Citation Analysis in the Evaluation of Scientific Activity by Francis Narin and Computer Horizons, Inc. (CHI) in 1976 (Narin, 1976), which has been regarded as a good review source for anyone interested in scientometrics (Garfield, 1979b).

Derek Price’s 1965 article ‘Network of Scientific Papers’ (Price, 1965) has been also regarded as a key event in the development of the field of scientometrics.

Michael Moravcslk (1977) presented a review of scientometric literature (Moravcslk, 1977).

Anthony van Raan (1997) analyzed the state of the art of scientometrics and characterized its application-oriented tradition. He envisaged that scientometrics could benefit significantly from a greater integration with knowledge discovery and data mining.

Loet Leydesdorff (2001) identified some challenges of scientometrics and suggested that: “the state of the art of science studies is ‘preparadigmatic:’ it is an interdisciplinary area integrated only at the level of its subject matter, and an applicational area for various contributing disciplines.”

A directly related study of Scientometrics was done by Olle Persson (2000). He retrieved 1,062 articles published in the journal from volume 1 to volume 44 between 1978 and 1999. Top-10 most cited publications include (Garfield, 1979a; Schubert, Glanzel, & Braun, 1989; Small, Sweeney, & Greenlee, 1985). He generated several maps to show a variety of structures, including journal co-citation, direct citation links among countries, shared citations among authors, and direct citations among authors.

This study is based on bibliographc data retrieved from the Web of Science. The data contain all types of documents published in Scientometrics between 1981 and 2001. ... Each article must be cited for 5 times or more in order to be included in this integrated analysis. This threshold resulted in a total of 403 articles.

In this study we have adapted an integrated procedure of citation analysis and information visualization, including Pathfinder network scaling, Principal Component Analysis (PCA), and visual-spatial models rendered in Virtual Reality Modeling Language (VRML).

The cocitation strength is computed as Pearson’s correlation coefficients to form a co-citation matrix. ... The co-citation matrix forms the basis of a base map, a terminology commonly used in cartography.

Factor analysis, namely PCA, is subsequently applied to the co-citation matrix in order to produce a thematic overlay. The purpose of such a thematic overlay is to highlight the density distribution of various specialties. Factor loadings are used to color code each publication in the thematic overlay.

We simplify the cocitation matrix using Pathfinder network scaling, which retains the strongest co-citation links with reference to the so-called triangle inequality condition (Chen, 1997, 1998; Schvaneveldt, 1990).

Finally, the growth of citation rates is animated across the entire Pathfinder network to facilitate the identification of trends. The visualization-animation model is made available in VRML 2.0 for easy access on the Internet.

PCA identified 25 factors from the 403 by 403 co-citation matrix. In theory, each factor should correspond to a specialty. ... The large number of factors reflects the diversity of scientometrics.

In our analysis, we focus on the three largest factors of significant specialties of the field.
Specialty 1: Citations in Science Studies.
Specialty 2: World and national science performance.
Specialty 3: Evaluation research outputs.

Peritz, B. C., & Bar-Ilan, J. (2002). The sources used by bibliometrics-scientometrics as reflected in references. Scientometrics, 54(2), 269-284.

Peritz, B. C., & Bar-Ilan, J. (2002). The sources used by bibliometrics-scientometrics as reflected in references. Scientometrics, 54(2), 269-284.

Van Raan (1997)討論科學計量學的最佳狀態，強調這個領域需要平衡應用與基礎研究以及增強科學計量學和廣泛的學科之間的關係。過去已經有許多研究根據Scientometrics期刊論文的參考文獻，對科學計量學領域的特性進行分析，例如：Schubert and Maczelka (1993)利用1980-81與1990-91兩個期間的論文參考文獻，分析1980年代Scientometrics期刊的改變，所使用的指標包括參考文獻的年齡分布(age distribution)、Price指標(Price index)、引用出版品的分布、引用作者的分布以及最常引用的出版品等。Wouters and Leydesdorff (1994)分析前25卷的論文參考文獻，計算論文的平均參考文獻數量以及被引用文獻的相對年齡(relative age)。Schoepflin and Glänzel (2001)以1980、1989和1997年的論文參考文獻資料，計算Price指標、參考文獻為連續出版品(serial)的比率、參考文獻的平均年齡(mean reference age)和平均引用的參考文獻比率(mean reference rate)。

本研究比較Scientometrics期刊1990和2000年論文的參考文獻，共169篇論文(1990年70篇，2000年89篇。附註：此處有誤，合計只有159篇。)，2814筆參考文獻，並將參考文獻的來源分為不同領域。結果發現1990年每篇論文平均有15.1(1054/70)筆參考文獻，2000年每篇論文平均有19.8(1760/89)筆參考文獻。Scientometrics期刊論文引用近五年的文獻比例，也就是Price指標，從1990年的37.6%，減少為2000年的31.6%。1990年有47.3%的參考文獻來自科學計量學與書目計量學(scientometrics and bibliometrics)、圖書資訊學(library and information science)以及社會學、歷史學與哲學(the sociology, history and philosophy of science)，2000年增加為56.9%。兩個年度的作者自我引用情形沒有明顯差異，1990年為13.4%，2000年則為13.9%。然而該期刊的自我引用(journal self-citation)與引用期刊的百分比(the percentage of references to journals)等情形皆有增加。期刊的自我引用情形從1990年的12.9%增加為2000年的20.1%，可能的理由是Scientometrics愈來愈成為這個領域的核心期刊。

The aim of this study was to examine the extent to which the field of bibliometrics and scientometrics makes use of sources outside the field.

The results show that in 2000, 56.9% (and 47.3% in 1990) of the references originated from three fields: scientometrics and bibliometrics; library and information science; and the sociology, history and philosophy of science.

When comparing the two periods, there is also a considerable increase in journal self-citation (i.e., references to the journal Scientometrics) and in the percentage of references to journals.

Van Raan (1997) discussed the state-of-the-art of scientometrics, and emphasized the need to balance between applied and basic research in the field, and the importance of strengthening the relations of scientometrics with a broad spectrum of disciplines.

Peritz (1981) examined the references of research papers published in 39 core journals during five calendar years, and calculated the percentage of references outside the field. In another study, Peritz (1988) examined the literature for bibliometrics for the period 1960–1985, and classified the body of literature according to the field of the journal in which the article was published.

Al-Sabbagh (1987) studied the interdisciplinarity of information science through a reference analysis of JASIS. The findings, based on a ten percent random sample of references appearing in JASIS articles between 1970 and 1985 show that the largest percentage of references come from information science, followed by computer science, library science and science-general.

Thompson (1989), based on the references of articles in twenty library and information science journals in five selected years, studied the age of the references, the extent of self citation, and found that the list of most cited journals was almost exclusively from library and information science.

Cronin and Pearson (1990), in a study based on citations found that information science exports techniques of information retrieval and bibliometrics.

Meyer and Spencer (1996) analyzed citations to twenty-four library and information science journals over a twenty-year period. Their findings show that 86.6% of the citations come from library and information science, but other disciplines including computer science, medicine, psychology, the social sciences and general sciences also cite library and information science journals to some extent.

Rousseau (1997) studied the references appearing in the papers of the first two ISSI Conferences and citations of the Proceedings. He tabulated the most frequently cited publications – the three most frequently cited publication were JASIS, Scientometrics and J. Doc. The list of most frequently citing journals and of the most cited papers from the Proceedings was also presented.

The journal Scientometrics has been the “theme” of several previous bibliometric studies.

Schubert and Maczelka (1993) studied the changes that occurred to the journal during the 1980’s based on its reference patterns. Two periods, 1980–81 and 1990–91 were chosen. They calculated the age distribution of the references, the Price Index, the distribution of the cited publication, the distribution of cited authors, and tabulated the most frequently cited publications.

Wouters and Leydesdorff (1994) analyzed the references of all articles and notes in the first 25 volumes of Scientometrics, and calculated, among other indicators, the number of references per article and the relative age of the cited literature (the Price Index).

Persson (2000) maps the citation and reference patterns of Scientometrics based on volumes 1 to 44 of the journal.

Very recently Schoepflin and Glänzel (2001) calculated several bibliometric measures (the Price Index, percentage of references to serials, mean reference age and mean reference rate) for articles, letters and notes published in Scientometrics in 1980, 1989 and 1997.

Most of the previous studies of Scientometrics were either concerned with quantitative aspects (e.g.; the Price Index) or with citation and co-citation patterns.

This study analyzed all the references of all the papers published in Scientometrics in 1990 and in 2000. The population of the study consisted of 169 papers and 2814 references. ...The list contained 70 items for 1990 and 89 items for 2000, altogether 169 papers. ... In the set for 1990, 60 (86% of the total for 1990) items in the list were labeled as articles, while for 2000, 83 (93% of the total for 2000) items were labeled as articles.

The references were categorized according to six facets:
• author self-citation;
• journal self-citation;
• discipline of publication source;
• field self-citation;
• type of publication;
• year of publication.

Thus, first we had to define the major themes covered by the field:
• indicators (science and technology), forecasting and planning;
• research trends, research evaluation and funding;
• science policy;
• bibliometric laws and models;
• citation analysis including all aspects (e.g. obsolescence, ranking, mappings, coupling, etc.);
• patent analysis;
• reference analysis;
• coword analysis in context of performance;
• productivity (e.g. authors, journals, institutions);
• impact;
• peer review process;
• sociology of science;
• social contexts of research;
• characteristics and development of a scientific area;
• scholarly communication;
• scientific networks;
• technology flow;
• innovation;
• other themes relevant to the field.

Altogether, 2814 references were identified, 1054 in 1990 and 1760 in 2000.
The mean number of references in 1990 was 15.1, while in 2000 the mean increased to 19.8.

It is interesting to note, that the percentage of references in the last five years (1986 to 1990, for 1990; and 1996 to 2000 for 2000) decreased from 37.6% to 31.6%. Does this mean that scientometrics is getting “softer” (the “Price Index”, (Price, 1970))?

Both Schubert and Maczelka (1993) and Shoepflin and Glänzel (2001) found that the Price Index of scientometrics increased over time. They, as in the current work, based their data on single years.

On the other hand, Wouters and Leydesdorff (1994) studied the first twenty five volumes of Scientometrics, observed some fluctuations in the Price Index over the years, but showed that the regression line is not significant, and concluded that the index displays neither rise or fall between 1978 and 1992.

We also believe, that in order to draw conclusions about the “hardness” or “softness” of the field, its journal or journals should be studied over a continuous time period, and not isolated years.

A reference for a given item was labeled as author self-citation, if one of the authors of the reference matched one of the authors of the given item. ... Author self-citation was 13.4% for 1990 (141 references) and 13.9% (244 references) for 2000. ... In terms of author self-citation, no significant differences were observed between the two periods.

Journal self-citation, (i.e., references to the journal Scientometrics), on the other hand, increased considerably, from 12.9% in 1990 (136 journal self-citations) to 20.1% (354 journal self-citations) in 2000.

A possible explanation for this increase is that the journal Scientometrics is more and more becoming the central journal of the field.

The top four journals appear exactly in the same order for both years, and they represent the main aspects of the field: the field itself, its relation to information and library science, to planning and management and to the sociology of science. These four sources cover 18.5% of the references in 1990, and 28.1% of the references in 2000.

In the list of most frequently cited publications we see mostly journals, but also books, handbooks, yearbooks, collections, proceedings and reports. ... Table 4 shows that the percentage of the references to journal articles increased considerably, while the percentage of the references to books, yearbooks and reports decreased. In 1990 there were no references to electronic sources, this category only appeared in 2000. It will be interesting to see whether the electronic sources are going to be referenced more extensively in the future.

Along with the increasing citation rate of the journal Scientometrics, we observe a general increase in sources belonging to the field of scientometrics and bibliometrics. This could either be the sign that the field is becoming more mature or self-sufficient or it may indicate that scientometricians base their research less and less on methods and studies conducted in other fields.

Most of the references 2000 (56.9%), and nearly half of the references in 1990 (47.3%) are from the three fields, closely related to the subject-matter: scientometrics and bibliometrics itself; library and information science; and sociology and history of science.

A substantial amount of references are to sources belonging to the social sciences (21.3% in 1990, and 13.0% in 2000). We see that the percentage of references to sources from the social sciences decreased considerably.

On the other hand, the combined share of sciences-general; mathematics, computer science, statistics and engineering; science and medical sciences remained nearly the same (23.4% in 1990 versus 23.5% in 2000).

About half of the references originate from sources, which are not related to scientometrics. It is quite possible that some of these references are to works, which belong to the field.

On the other hand, some of the references from the fields closely related to scientometrics are not classified as field self-citation.

In 1990, 593 out of the 1054 references (56.3%) were classified as field self citation, while in 2000, 1092 out of the 1760 references (62.0%) were field self-citations. This is a rather considerable increase, it may indicate that the field is becoming more and more self sufficient, and needs to rely less on theories and methods emanating from other scientific fields.

The results show that the field relies heavily on itself, on library and information science and on sociology, history and philosophy of science.

There is an increase in journal self-citation, the list of core journals remaining stable for both periods. Author self-citation is around 20% for the years under study.

Schubert, A. (2002). The web of scientometrics: A statistical overview of the first 50 volumes of the journal. Scientometrics, 53(1), 3-20.

Schubert, A. (2002). The web of scientometrics: A statistical overview of the first 50 volumes of the journal. Scientometrics, 53(1), 3-20.

Schoepflin and Glänzel (2001)將Scientometrics期刊上的論文主題分為六類：1. 理論(包括書目計量學理論、數學模型和書目計量學定律的公式化)、2. 案例(案例研究和實務論文)、3. 方法學與應用、4. 指標 (指標工程與資料呈現)、5. 社會學取向(書目計量學的社會學方法和科學社會學)以及6. 政策研究(包括科學政策、科學管理和一般與科技討論)等，並應用這樣的分類討論Scientometrics期刊的論文主題在1980、1989及1997等三個不同年份的比重變化。上述的主題可以在歸納成核心的書目計量學(core bibliometrics) (2, 3, 4)和背景研究(background research) (1, 5, 6)或是理論研究(1, 3, 5)和應用研究(2, 4, 6)。Schoepflin and Glänzel (2001)的研究發現案例與方法學有明顯與穩定的成長，但社會學取向和科學政策的論文數量減少。

本研究以前50卷1443篇Scientometrics期刊的論文資料進行計量分析，這1443篇論文上包含來自60個國家的1223位作者，共包含25200個參考文獻(不重複的項目共16500個)，除了382篇論文不曾被引用外，1061篇曾被引用的論文共被引用7242次。

以合作關係的分析，55.1%的論文為單一作者，只有5.4%的論文由3位以上的作者合作完成，論文的平均作者數是1.61，過去十年來合作的情形略有增加，因此科學計量學領域比較像社會科學或數學。國際合作情形較罕見，只有 7%的Scientometrics論文由一個以上的國家合作完成，但後25卷的國際合作情形是前25卷的兩倍以上。

以Price指標(the Price index)分析Scientometrics論文的25200個參考文獻，了解其引用文獻在五年內出版的比例，藉以判斷該期刊是屬於軟科學(soft sciences)或硬科學(hard sciences)，結果發現在五年內出版的文獻大約占45%。其引用的期刊來源，除了圖書資訊學(Information Science & Library Science)、電腦科學(Computer Science)和跨領域應用(Interdisciplinary Applications)外，其餘多屬普通物理(general physics)、普通化學(general chemistry)、普通醫學(general medicine)等較廣泛的領域。但也可以發現有相當高比例(13.6%)是期刊自我引用的情形，就被引用的情形來看則高達47.3%是來自期刊本身。

A total of 1443 items were published by 1223 authors from 60 countries. They contained 25200 references to about 16500 different items ...1061 Scientometrics papers received 7242 citations during the 1978-2000 period (i.e., 382 papers remained uncited).

Activity Indexes show even more spectacularly the outstanding relative activity of a few countries (first of all Hungary, but also Belgium, Bulgaria, Chile, Mexico, Netherlands and, if measured against social science standards, then P.R. China, Poland, Russia and Spain, as well). A conspicuously low relative activity is shown by Italy and Japan (not so much if social science standards are concerned); interestingly, most English-speaking countries (US, England, Australia, Canada) exhibit a lower-than-average activity.

In their paper, Schoepflin and Glänzel (2001) classified the papers published in Scientometrics into six thematic categories, and studied the change in the weight of these categories by selecting three sample years: 1980, 1989 and 1997.
They used the following categories:
1. (THEO) Bibliometric theory, mathematical models and formalisation of bibliometric laws;
2. (CASE) Case studies and empirical papers;
3. (METH) Methodological papers including applications;
4. (INDI) Indicator engineering and data presentation;
5. (SOCI) Sociological approach to bibliometrics, sociology of science;
6. (POLI) Science policy, science management and general or technical discussions.

The classification permitted to group the material in several ways: the categories can be regarded from the viewpoint of core bibliometrics (2, 3, and 4) and background research (1, 5, and 6), but also with respect to theoretical (1, 3, and 5) and applied research (2, 4, and 6).

There are two obvious developments: an impressing and steady growth of case studies (Category 2) and methodology (Category 3) and the loss of position of articles on sociological (Category 5) and science policy (Category 6) issues.

In this respect, however, scientometrics resembles rather to the social sciences(or, maybe, mathematics) than to the sciences: 55.1% of the papers published in the first 50 volumes of Scientometrics is single-authored and only 5.4% of them are multi-authored (more than 3 authors). The average number of authors per paper is 1.61. In the past decade, nevertheless, there is a slight tendency of growing collaboration (see Figure 3).

International collaboration is even less favourised in the scientometrics community. There is a modest 7% of Scientometrics papers having more than one country in the authors’ affiliation section in the by-line of the publication. Nevertheless, the tendency is unambiguous: the fraction of internationally co-authored papers more than doubled from the first to the second 25 volumes.

The 25200 references of the papers form, as it were, the intellectual “hinterland” of research reported in Scientometrics. Clearly, they constitute a vast treasury of information about the history, sociology, epistemology of the field and its journal, and several attempts were made to make the most of this information (Schubert and Maczelka, 1993; Wouters and Leydesdorff, 1994; Schoepflin and Glänzel, 2001).

De Solla Price (1970) introduced an index, later named after him, with the aim of distinguishing between “harder” and “softer” sciences. The Price Index is defined as the percentage share of references to items not older than five years at the time of publishing the citing paper. Typical “soft science” journals (German Review, American Literature, Studies in English Literature, Isis, in Price’s original study) have an index value less than 10%, while, e.g., some research front physics journals may reach 80%.

The Price Index of the journal Scientometrics is around 45%, i.e., it occupies a medium position on the hardness scale.

Among the highly cited sources there is a clear dominance of SCI/SSCI-covered titles: 19 of the 25 titles belongs to this category. References to the journal itself (journal self-references) constitute 13.6% of all references – it is a typical value for a consolidated primary journal.

The journal Scientometrics itself is categorised into two subfields: Information Science & Library Science and Computer Science, Interdisciplinary Applications.

It can be seen that Information Science & Library Science would remain the main source of information for the journal even if self-references were disregarded, while Computer Science, Interdisciplinary Applications would disappear from the chart without them.

Among science journals, those from broader-scope subfields (general physics, general chemistry, general medicine, multidisciplinary sciences) contribute the most to the reference base of Scientometrics. The presence of Analytical Chemistry among the top cited fields may be connected with the fact that this is the original and main fireld of the Editor-in-Chief of the journal.

The journal self-citation rate of 47.3% is rather high, particularly if compared with the self-reference rate of 13.6%. It indicated that the “outside world” pays less attention to the journal than vice versa.

Dutt, B., Garg, K. C., & Bali, A. (2003). Scientometrics of the international journal Scientometrics. Scientometrics, 56(1), 81-93.

Dutt, B., Garg, K. C., & Bali, A. (2003). Scientometrics of the international journal Scientometrics. Scientometrics, 56(1), 81-93.

過去關於科學計量領域的計量分析結果：Wouters and Leydesdorff (1994)根據Price指標的分類，指出科學計量學並未成為一門硬性的社會科學(hard social science)，Schoepflin and Glänzel (2001)則認為這個領域的異質性很高，每一個次領域都有它本身的特性。

本研究針對以下的問題進行探討：確認Scientometrics期刊上1978到2001年發表論文資料的主題，分析這期間論文的分布情形，不同國家在不同主題上的貢獻，具有主要生產力的機構，並藉由合著關係發掘國內與國際間的合作情形。本研究將論文的主題分為科學計量評估(scientometric assessment)、引用與叢集分析(citation and cluster analysis)、科學計量分布(scientometric distribution)、科學的歷史(history of science)、科學合作(scientific collaboration)、科學計量學的理論研究(theoretical studies on scientometrics)，不在上述主題的論文則歸類為其他。

結果發現：論文數最多的主題是科學計量評估(scientometric assessment)，這個現象反映出科學政策的制定逐漸運用科學計量工具的事實，其次是理論研究(theoretical studies)。在前期(1978-1986年)，科學的歷史(history of science)方面的論文較多，其次是引用與叢集分析(citation and cluster analysis)；科學計量分布(scientometric distribution)在前期與中期(1987-1994年)都相當重要，但後期(1994-2001)逐漸減少；後期具有最重要地位的主題則是科學合作(scientific collaboration)。

美國是目前生產力最高的國家，共占17.7%的論文，主要的8個歐洲國家則共佔47.6%，但美國在論文所佔的比例逐年減少，加拿大與前蘇聯有同樣的情形，但荷蘭、印度、法國和日本在上升中；從每一個機構平均發表的論文數可以看出這個領域的生產力相當分散，1317篇論文的作者資料共來自1538個機構，但是有1109篇論文是單一機構發表，兩個或以上的機構發表的論文只有208篇；在1538個機構中，發表超過15篇或以上論文的機構共有8個，匈牙利和荷蘭各有2個，其餘的4個機構分別位於印度、比利時、英國和美國；雖然目前的論文以單一作者為主，論文的平均作者數僅為1.73，但多位作者的論文雖然僅占18%，但正逐漸增加。

The study indicates that the US share of papers is constantly on the decline while that of the Netherlands, India, France and Japan is on the rise.

The research output is highly scattered as indicated by the average number of papers per institution.

The scientometric output is dominated by the single authored papers, however, multi-authored papers are gaining momentum.

However, Wouters and Leydesdorff [1] presented a combined bibliometric and social network analysis of papers published in first 25 volumes of Scientometrics, and concluded that scientometrics has not become a hard social science as reflected by the values of Price Index.

In another study, Schoepflin and Glänzel [2] point out that the field of scientometrics is heterogeneous, and each sub-discipline has its own characteristics.

The objectives of the study are:
(i) to identify the scientometric themes on which papers have been published in volumes 1(1978) to 50 (2001), and to find out as to how the emphasis on different themes have changed during different periods;
(ii) to examine the distribution of output of different countries during 1978 -2001, and to analyse the change in the trend, if any;
(iii) to study the relative research emphasis of different countries on different scientometric themes;
(iv) to identify the most productive institutions, and to study the scientometric themes they have dealt with;
(v) to study the pattern of co-authorship and the pattern of domestic as well as international collaboration.

The entire data set was classified into seven groups:
scientometric assessment;
citation and cluster analysis;
scientometric distribution;
history of science;
scientific collaboration;
theoretical studies on scientometrics.
Papers which could not fit into these categories were kept under ‘others’.

An analysis of the data indicates that about one-third of the papers published in Scientometrics deal with scientometric assessment which mainly include cross-national, national and institutional assessment, besides evaluation of journals, bibliometric performance indicators, funding and performance, and S&T indicators. This was followed by theoretical studies (Table 1).

From the values of the Activity Index presented in Table 1, it is observed that the priorities of different themes kept changing during different periods. For instance, during 1978-1986 ‘history of science’ followed by ‘citation and cluster analysis’ were the areas of maximum emphasis.

Studies dealing with ‘scientometrics distribution’ got almost the same priority during 1978-1986 and 1987-1994, but emphasis on this theme has gone down considerably in the last block.

During 1994-2001, studies dealing with ‘scientific collaboration’ got maximum priority followed by ‘scientomeric assessment’.

Major contribution (>=2%) of the total output came from 13 countries listed in Table 2. The distribution of papers presented in Table 2 indicates that USA tops the list of publications which are 17.7 per cent of the total world output.

The values of the Activity Index for different countries (Table2) indicate that during the last two blocks, i.e. 1987-1994 and 1994-2001, the productivity of the USA has declined considerably. Similar is the case with Canada and the former USSR.

Further analysis of data presented in Table 2 indicates that Scientometrics is getting Euro-centred, as 8 countries of Europe listed in Table 2 have contributed 47.6 per cent of the total output. The share may be greater, if the output from other European countries not listed in Table 2 is included.

The total output of 1317 papers published in 50 volumes of Scientometrics came from 1538 institutions. 1109 papers were published involving only a single institute and the rest 208 involved collaboration either with 2 or more institutes.

Number of such institutes which published 15 or more papers is only 8 and their share in the total output is 259 (19.66 %). Of the 8 prolific institutions, two are from Hungary, two from the Netherlands, and one each from India, Belgium, UK and USA.

The results presented in Table 5 indicate that slightly more than half of the papers were single authored and the rest were written by either two or more authors. The share of multi-authored papers (>=3) is much less (18%) only as compared to single or two authored papers.

A study carried out by Cunningham and Dillon [7] for authorship pattern in library and information science indicates average number of authors per paper for information science is 1.17. In scientometrics the average number of authors per paper is 1.73 which indicates a better collaboration than library and information science.

The values of DCI for Spain, France, India and Japan were much higher than the world average indicating a good domestic collaboration. However, except Spain all these countries had very low values of ICI, which indicates that these countries have a poor international collaboration. On the other hand UK, Hungary, Belgium, Canada and Germany had good international collaboration as reflected by the values of ICI.

The focus of scientometric studies is shifting from the history of science and scientometrics distribution to scientific collaboration and scientometric assessment.

Scientometric assessment constitutes about 34% of the total output of the papers which is the highest among all the themes. Emphasis on scientometric assessment studies reflects the growing realisation of its utility as a tool for science policy making.

van den Besselaar, P. (2001). The cognitive and the social structure of STS. Scientometrics, 51(2), 441-460.

van den Besselaar, P. (2001). The cognitive and the social structure of STS. Scientometrics, 51(2), 441-460.

本研究利用作者共被引分析(author cocitation analysis)分析STS領域的社會結構，探討做為次領域間連結的作者或研究機構。本研究將STS領域分為STS的量化研究次領域(the qualitative STS sub-field)、STS的質性研究次領域(the qualitative STS sub-field)和政策導向次領域(the policy oriented sub-field)，並且以Scientometrics期刊為STS的量化研究的代表，Social Studies of Science和Science, Technology and Human Values兩種期刊代表STS的質性研究，Research Policy則是STS政策研究的代表。針對1986到1997年間在這些期刊上被引用超過25次的229位作者，建立他們的共被引矩陣，然後進行因素分析(factor analysis)，查看這些作者被歸類的情形，並且與上述的次領域進行比較分析。此外，本研究也探討被不同次領域引用的作者、不同次領域之間的作者的合作關係以及有多少位作者在不同的次領域發表論文？

Table 1表示762、305、304及569位作者分別曾在Scientometrics、Social Studies of Science、Science, Technology and Human Values以及Research Policy等期刊發表論文，Scientometrics和Research Policy的作者平均在對應的期刊上發表1.5及1.7篇，比Social Studies of Science和Science, Technology and Human Values的1.1篇來得高。曾在四種期刊發表論文的作者則是1756位，平均每位作者發表的論文數為1.4。

共有759個機構曾在四種期刊上發表論文，但只有少數的機構有較高的生產力，例如超過11篇論文的機構僅有41個。此外，從Table 2也可以發現有些高生產力機構的論文是在不同次領域的期刊上發表。

共有65個國家在四種期刊上發表論文，其中大多數的國家(57個)有在Scientometrics上發表，但其他三種期刊都僅有約半數的國家有發表的紀錄。

將作者共被引矩陣進行因素分析後，較大的因素共有7個，依作者撰寫論文的內容將各因素命名。其中第1個因素和第6個因素間有很大的關係，第1個因素有大半數的作者的次高負荷是落在第6個因素上，反之亦然，第1個因素和第6個因素的研究主題為科技政策相關的STS研究。第2個因素的研究主題為STS的質性研究。第3個因素和第4個因素、第5個因素以及第7個因素彼此間的作者有關係，這些因素可以視為是STS的量化研究，進一步來說，第4個因素、第5個因素和第7個因素的主題分別是科學社會學(Sociology of Science)、詞語共現分析和資訊計量學。

接下來，Table 4 分析各次領域的專家(specialists)以及兼通兩門或以上的通才(generalists)。本研究將專家定義為在該次領域發表的論文數超過該領域論文總數0.69%以上的作者，STS的量化研究、質性研究和政策導向研究等次領域各有31、23和41位。量化研究次領域的專家並且也發表質性研究相關論文的作者有6位，反之質性研究次領域的專家並且也發表量化研究相關論文的作者只有2位。量化研究次領域的專家同時發表政策導向相關論文的作者有14位，政策導向研究次領域的專家並且也發表量化研究相關論文的作者則有11位。從以上數據顯示，量化研究與其他兩個次領域的關係主要是由量化研究次領域的研究者在維繫著，也就是量化研究次領域的研究者是主要的跨邊界者(boundary spanners)。

Table 4上也可以發現一些從質性研究次領域跨越政策導向研究的研究者，這個研究結果修正了先前認為質性研究次領域比較獨立的看法。

The differentiation of scientific fields into sub-fields can be studied on the level of the ‘scientific content’ of the sub-field, that is on the level of the products, as well as on the level of the ‘social structures’ of the sub-field, that is on the level of the producers of the content.

By comparing the behavior of the constructs with the behavior of the constructors, we are able to demonstrate the analytical distinction between a cognitive and a social approach in an empirical way.

Although we are able to distinguish analytically between the cognitive and social dimension of the development of the research field, we find similar patterns of differentiation on the social level too. At the same time, this differentiation differs in some respects from the cognitive differentiation pattern.

Consequently, the social and the cognitive dimensions of the STS field are not independent – as no serious STS scholar would argue – but also not identical, as radical constructivists claim, but are strongly interacting.

It was claimed that scientometrics has to focus more on the role it can play for qualitative STS, and that scientometric researchers should refrain from sterile data and mathematics. It was felt that scientometric results have to be carefully interpreted from a substantial perspective, to be meaningful for S&T policy.

There, we showed that the journals Social Studies of Science (SSS) and Science, Technology and Human Values (STHV) form a reasonable operationalization of the qualitative STS sub-field. Research Policy represents the policy oriented sub-field, and Scientometrics can be used as a representation of the quantitative STS sub-field. These journals are central in STS as they have the highest impact factors in their respective sub-fields.

In this paper we will use the same boundary of STS to analyze the social structure of the field: who are the authors and what are the research groups in the field as defined by the mentioned journals? Do they function as the ties between the various sub-fields?

Data about authors and institutional affiliation can be found on the CD-ROM version of the Social Science Citation Index (SSCI). We downloaded the full records for all publications in the four journals for the period 1986-1997.* This resulted in a database with 3579 records. ... Finally, as is usual in scientometric studies, for further analysis we restricted the database to Articles, Reviews, Notes, and Letters, and excluded other document types. This resulted in a final set of 1787 documents.

Referring to a text may indicate the use of a knowledge claim to support one’s own position, or to oppose to. Referring to persons, on the other hand, may indicate the existence of a social relationship. Therefore we will use author co-citation analysis as a first methodology to analyze the social structure of the STS field. In this way, we will describe the STS field in terms of clusters of authors that are placed near each other by the scholars active in the field.

Using the prepared database and bibexcel, an author co-citation matrix has been produced of all cited 229 authors with more than 25 citations over the 1986-1997 period. Factor-analyzing (principal component analysis, varimax rotation with Kaiser normalization) this matrix results in clusters of authors, and the question is whether these clusters differ from the three sub-fields of qualitative, quantitative, and policy oriented STS.

If a communication system shows considerable segregation, individual researchers (or institutes) could play the role as weak ties [3] between the sub-fields.

(i) Authors can refer to materials from other sub-fields. We classify these authors as being active on the borders of the sub-fields. The border between sub-fields A and B is then defined as the authors of papers in sub-field A referring to papers in sub-field B, and the other way around. How densely populated are the borders between the subfields?

(ii) Authors can cooperate with colleagues active in the other sub-fields. Even if authors specialize, research groups and institutions may cover more sub-fields, and this could indicate social integration of the field on a more informal level of communication.

(iii) Generalist authors work in various sub-fields. Do many authors publish in more than one sub-field, or do we see a specialization and differentiation on the level of individual scholars? How many generalists can be found among researchers and institutions? The larger numbers we find, the stronger is the degree of communication between the sub-fields.



The average number of authors per article is 1.4, but this figure is higher in Scientometrics (1.5) and in Research Policy (1.7), but considerable lower (1.1) in the two qualitative STS journals.



As expected, the number of frequently publishing institutes is rather small, compared to the grand total.

If we aggregate one more step, to the level of countries, we find 65 countries active in the STS field, of which some 57 are active within scientometrics. However, only half of the countries are publishing in the qualitative journals SSS and STHV. The same is true for Research Policy.

Factor analyzing the author co-citation matrix resulted in a solution of 22 factors with an eigenvalue larger than 1. Inspecting the scree plot shows that seven factors dominate the structure, and these factors explain more than 70% of the total variance. More than 90% of the 220 cited authors have their highest factor score on one of these seven factors.



The authors in Factor 1 are within science & technology policy studies and in research & innovation management studies, or in related fields in management and economics. The same holds for the small Factor 6. Half of the authors in Factor 1 have a relatively high second factor loading in Factor 6, and all authors with their highest loading on factor 6 do have a high second loading on Factor 1.

Authors with their highest factor loading on Factor 2 all belong to qualitative STS, and they generally do not load on other factors.

Factor 3 represents quantitative STS. Most authors with the highest loading on Factor 4 can be characterized as traditional sociology of science (e.g., Merton). Factor 5 represents coword analysis, and Factor 7 represents informetrics and scientometric distributions (e.g., Bradford and Lotka). Between the Factors 3, 4, 5, and 7 we find a considerable ‘interfactorial complexity’: the authors loading highest on Factor 3 often have a substantial second loading on one of the Factors 4, 5, or 7. The same is true the other way around.

Therefore I also created the author co-citation matrix of all authors with more than 25 citations over the whole period with the highest loading on the Factors 3, 4, 5, or 7. Authors that have a second loading on these factors of more than 0.2 are also included. This set of authors represents the sub-field scientometrics.

Factor-analyzing this matrix in a similar way results in seven substantial factors. Inspection of the factors shows that they represent the following research foci: Policy oriented scientometrics (Factor 1); Empirical science & technology studies (Factor 2); Coword analysis (Factor 3); Scientometric distributions (Factor 4); Critique of scientometrics (Factor 5); Patent studies (Factor 6); Economics of technical change (Factor 7). This result corroborates that the method is suited for analyzing the fine structure of research fields.

If we now summarize these findings, the factor-structure of the co-citation matrix of STS reproduces the clear split between policy oriented STS (Factor 1 plus 6), qualitative STS (Factor 2), and quantitative STS (Factors 3, 4, 5, 7), while at the same time showing some internal differentiation in the sub-field of scientometrics. In other words, the author co-citation analysis reveals a similar structure as the journal-journal citation analysis did.8

Firstly, we distinguish the groups of specialists, which consist of the authors with relatively high numbers of publications in one of the various sub-fields of STS. We consider a scholar as specialist in one of the sub-fields, if he or she is (co-) author of at least 6, 4, or 3 publications respectively in quantitative, qualitative, or policy oriented STS. In this way, the threshold is about the same in the three sub-fields: 0.77%, 0.69%, and 0.73%.

Secondly, we have the semi-generalists, the groups of authors active in two of the three sub-fields each. A semi-generalist is defined as an author who has published at least two publications in two of the three sub-fields.

Finally we have the group of generalists, publishing in all the three sub-fields, again based on at least two publications per sub-field.




The number of specialists in Scientometrics is 31, and only six of them have published in SSS or STHV. The other way around we identified only 2 authors. This implies that the more quantitative researchers maintain the relations between these two sub-fields

The number of Scientometrics authors also publishing in Research Policy is much higher, and some 45% of the scientometrics specialists also work – at least incidentally – on S&T policy topics. Researchers frequently publishing in Research Policy publish a little less (27%) in Scientometrics, but this is still a substantial number.

This underlines our earlier conclusion that research policy and management is related to scientometrics for the part of using scientometrics in research evaluation, but not much wider.8

Between Scientometrics and Research Policy, as well as between Scientometrics and SSS/STHV, most of the authors who maintain the relation have most publications in Scientometrics, and generally only a single publication in one of the other journals. This implies that the relations between the sub-fields (also the very weak one’s) are maintained to a large extent by scientometricians.

Between Research Policy and SSS/STHV the picture is more balanced, with a weak emphasis on the SSS/STHV authors. The number of authors publishing both in qualitative STS and S&T policy studies is very low, as is the number of authors publishing both in quantitative STS and in qualitative STS. Only the number of authors publishing in both quantitative STS and S&T policy studies is substantial.

Lowering the threshold increases the number of (semi-)generalists, but of course most of them have a very low number of publications, and the scientometricians are the boundary spanners, much more than the others.

However, a larger number of qualitative authors than expected is also active in the S&T policy studies. Only this latter finding modifies slightly our earlier conclusion that qualitative STS is an isolated sub-field.

We use a 3% threshold, and various organizations that exceed this threshold in one of the sub-fields are in Table 6. Three of the eight organizations are specialized in only one sub-field. Four others are specialized in two sub-fields, and only one organization is a generalist one, and active in three sub-fields.

In other words, there is a relatively low level of specialization here, as most of the institutions seem to be rather active in more sub-fields.

If we decrease the threshold to 2%, another 15 institutions count as specialists. However, of these 15 institutions only a few are active in more sub-fields. This implies that the most productive institutions within STS are also the broadest in their covering of the field.

Where the cognitive analysis showed that the relationship between scientometrics and S&T policy studies is stronger than the relations between qualitative and quantitative STS,8 on the level of the conferences (and as shown before, on the level of research institutes) it is the other way around. In other words, the institutional structures and the cognitive structures are not identical.

If we summarize the findings, we see that the cognitive patterns of integration and (mainly) differentiation to a large extent are visible within the social structure of the field.

The social relations between quantitative STS and policy oriented STS are similar to the cognitive relations between the two sub-fields. The links, however, between the two sub-fields are only between a substantial part of scientometrics and a small part of S&T policy studies, namely the part focusing on evaluation and performance studies.

The larger part of S&T policy studies is on technological innovation and on evolutionary approaches to technical change, and these research topics are not related to the research front in scientometrics, as the author co-citation analysis underlines.

Most importantly, we found that the interaction between qualitative and policy oriented STS is much stronger on the social level of authors and institutions than on the cognitive level of documents.

This may explain why the discussants in the panel session quoted earlier in this paper saw different divides, than the one’s I revealed in Ref. 8: the social structure of the STS field is not identical to its cognitive structure.

Within the mainstream of STS it is generally accepted that the production of knowledge and the grounding of knowledge claims consists of a ‘seamless web’ of cognitive and social elements.

Glenisson, P., Glänzel, W., Janssens, F., & De Moor, B. (2005). Combining full text and bibliometric information in mapping scientific disciplines. Information Processing & Management, 41(6), 1548-1572.

Glenisson, P., Glänzel, W., Janssens, F., & De Moor, B. (2005). Combining full text and bibliometric information in mapping scientific disciplines. Information Processing & Management, 41(6), 1548-1572.

本研究以詞語共現分析(co-word analysis)，將Scientometrics期刊2003年發表的論文，歸類為六個叢集。為了瞭解叢集結果的有效性，將這個結果與專家歸類的結果進行比較，同時也利用書目計量指標分析各個叢集。

Braam, Moed, and Van Raan (1991)建議利用詞語分析(word analysis)評估共被引叢集分析的結果，這些詞語利用書目紀錄裡的索引詞(indexing terms)和分類碼(classification codes)作為基礎。

在專家歸類方面，本研究援引Schoepflin and Glänzel(2001)研究的六個類別：數學模型與資訊計量學法則(Mathematical models/informetric laws)、個案研究(Case studies)、科學計量學的進展(Advances in Scientometrics)、指標工程(Indicator engineering)、社會學方法(Sociological approaches)與政策相關議題(Policy relevant issues)。加上近年興起的網路計量學(Webometrics)後，本研究用來進行專家的歸類的類別為：科學計量學的進展(Advances in Scientometrics)、實務論文與個案研究(Empirical papers/case studies)、數學模型(Mathematical models)、政策議題(Political issues)、社會學方法(Sociological approaches)以及資訊計量學與網路計量學(Informetrics/Webometrics)。下表是共詞分析與專家歸類的比較結果：

除了較大的A與E類別分布在多個叢集外，較小的類別大多集中內一個或兩個叢集上。

從六個叢集上的論文在專家以及它們的詞語網絡，可以將這些叢集分別：叢集1是書目計量學指標的方法學研究(methodological indicator research)，這些指標用來測量發表活動(publication activity)以及引用影響(citation impact)的研究；叢集2大多為有關於國家和機構方面或科學領域的個案研究(case studies)與實務性的論文(empirical papers)；叢集3和叢集1同樣是理論與方法學問題相關的論文，但更著重在資訊計量學法則(informetric laws)、頻率分布(frequency distributions)與多變化統計(multivariate stattistics)等先進方法學技術。叢集4是網路計量學和其他網路相關議題；叢集5是論文數較少的叢集，總共僅包括3篇論文，這些論文與共被引分析以及其他引用統計的分析有關；叢集6則是最大的叢集，包含的面向相當廣泛，從社會學、政策到科技等許多相關主題。從上述的分析，可以了解科學計量學目前主要的兩個面向是基於科學計量學標準技術的方法學研究和擴展傳統書目計量學範圍的實務研究。

接著利用平均參考文獻年齡(mean reference age)和連續出版品所占部分(share of serials)等書目計量學特徵分析上述的叢集結果。如下圖所示

在各個叢集裡，網路計量學具有低參考文獻年齡的特徵，並且連續出版品所占部分為中到高。政策議題相關的論文大部分具有相對低的連續出版品所占部分，但另有一群論文的連續出版品所占部分則明顯地高，因此相關的論文在圖形上分成兩個子叢集。至於科學計量學的先進方法與技術，除了少數例外，大部分的論文的平均參考文獻年齡在5到15年間，連續出版品所佔的部分則是在50%到90%間。實務性研究的論文在連續出版品所占部分的特徵分為兩群，一群的連續出版品所佔部分較低(<=55%)，另一群則較高(>=67%)，較低的一群與政策相關研究具有類似的特徵。

The question how bibliometric measures can, in turn, be assumed to reflect formal characteristics of documented scientific communication that might supplement results obtained from content-based analyses could also be answered in a positive way. Reference-based citation measures can help to fine-structure clusters determined on basis of co-word analysis.

Braam, Moed, and Van Raan (1991) suggested combining co-citation with word analysis in the context of evaluative bibliometrics to improve efficiency of co-citation clustering. The word analysis by Braam et al. used publication ‘‘word-profiles’’ that were based on indexing terms and classification codes.

Not much later, Noyons and Van Raan (1994) and Zitt and Bassecoulard (1994) demonstrated the appeal of plunging into contents by using keywords from both patent—and scientific literature to characterise the science-technology linkage.

The study by Schoepflin and Glänzel aimed at monitoring and characterising structural changes in the research profile in bibliometrics in the period 1980–1997. The authors created five categories, Mathematical models/informetric laws, Case studies, Advances in Scientometrics, Indicator engineering, Sociological approaches and Policy relevant issues. The term Webometrics did not yet appear in this scheme since at that time it was not yet established as a sub-discipline of scientometrics/informetrics.





We see classes S, M, I and P, admittedly all of smaller size, moderately to well conserved in the text-based cluster structure. Conversely, papers assigned to the larger classes A and E are heavily shifted around the text clusters.

The map in Fig. 6 represents the content structure of cluster 1 with altogether 9 papers. This cluster represents publications that are concerned with methodological questions related to bibliometric indicators. Indicator-related terms such as indicator names and terms relevant in the context of measuring publication activity and citation impact are close to the centre, and strongly interlinked. ... One could consider this cluster representing methodological indicator research.



Cluster 2 is dominated by empirical papers and case studies (cf. Table 3). ... The terms in this map are presented in Fig. 7 and relate above all to national and institutional aspects as well as to science fields. This is the cluster of case studies and traditional bibliometric applications.



Cluster 3 is a second theoretical/methodological cluster. Unlike the first one, this cluster relates to more advanced methodological techniques, such as informetric laws, frequency distributions and multivariate statistics. This cluster could be characterised as theoretical and mathematical issues in bibliometrics. The term structure is presented in Fig. 8.




Cluster 4 presented in Fig. 9 clearly represents webometrics and network-related issues. All terms are strongly interlinked. This cluster corresponds by and large to the category of Webometrics/Informetrics.



Cluster 5 with 3 papers is the smallest one. Co-citation analysis and the analysis of other citation statistics are the topic of these papers. The term structure (cf. Fig. 10) reflects the statistical vocabulary used in these studies. This cluster covers specific applications of statistical methods.



The last cluster with 30 papers (see Fig. 11) is by far the largest one. It comprises technology and innovation related studies, the science-technology interface and almost the complete Triple Helix issue can be found here (cf. Table 3). Also the sociological approaches are covered by this cluster. This cluster can be considered a borderland of classical scientometrics, namely the interdisciplinary approaches such as sociological, policy relevant and technology related issues.



The two large categories A and E covering 65% of all papers proved heterogeneous. Category A has (jointly with category M) three sub-clusters, namely, Cluster 1, 3 and 6, whereas Category E falls apart into three other sub-clusters: Cluster 2, 5 and 6. Policy relevant issues are also covered by clusters 2 and 6. Only Category I is represented by a corresponding co-word cluster, namely cluster 4.

The full text analysis substantiates that both methodological and empirical research have nowadays at least two different main focuses each, one is based on scientometric standard techniques such as classical indicators, the other ones are clearly broadening the scope of traditional bibliometrics.



As already seen in the pilot study, Webometrics is characterised by low reference age and medium–high share of serials (cf. Glenisson et al., 2005).

Most of the policy related issues are characterised by relatively low share of serials. Nevertheless, there is a group of papers with clearly higher share, too. This confirms the results of the full text analysis, namely that this category practically forms two sub-clusters.

The category Advances in Scientometrics proves strikingly homogeneous with several outliers only. Most of the A-class papers have, however, a mean reference age ranging between 5 and 15 years, with medium–high share of serials ranging between 50% and 90%.

The empirical groups proved heterogeneous, indeed. Regarding the share of serials this class forms two distinct sub-classes, particularly, one with low share (<=55%) and one with relatively high share (>=67%). The class with lower share has similar characteristics as the policy relevant class.

The question how bibliometric measures can, in turn, be assumed to reflect formal characteristics of documented scientific communication that might supplement results obtained from content-based analyses could also be answered in a positive way. Reference-based citation measures can help to fine-structure clusters determined on basis of co-word analysis.