Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the Association for Information Science and Technology, 54(13), 1237-1249.

Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the Association for Information Science and Technology, 54(13), 1237-1249.

本研究提出的方法特別關注學科之間的相互關係，提供了一個所有科學學科的總體概述。根據基於ISI(Institute for Science Information)的一系列多重學科分類指標，測量學科和研究領域的跨學科性，進而建立一個初步的學科和研究領域型態學(typology)。以研究領域和類別上的連結數量描述它們的相關類別數量及接近或遙遠的類別，多樣性和連結強度等方面的測量。

跨學科性問題的研究可以利用多種不同方法，例如通過訪談和調查(Hargens, 1986; Palmer,1999)，或是對於高等教育系統的組織分析和研究團隊的實證分析等(Sanz et al., 2001)。也可以利用書目計量學方法，特別是利用詞語、作者或參考文獻做為共現分析資訊，確定不同子領域之間的結構關係，並將其呈現為圖形表示的“科學地圖”(例如Tijssen, 1992; Small, 1999; Weingart & Stehr, 1999)。還有利用來自不同學科作者或中心之間的合作((Qiu, 1992; Qin et al., 1997; Bordons et al., 1999)，以及著重參考文獻或引用在類別上的分布(Porter & Chubin, 1985; Urata, 1990; Tomov & Mutafov, 1996; Bourke & Butler, 1998; Van Leeuwen & Tijssen, 2000; Van Raan & Van Leeuwen, 2002; Rinia et al., 2002)。上述的研究中通常將學科視為是期刊的集合(journal sets)，“跨界”(boundary crossing)的研究者便被認為是那些在本身學科以外的期刊上發表的作者。在Pierce(1999)關於社會學和政治學的研究中，發現跨界作者偏向來自鄰近學科，並從論文的被引用率可以證實，這些作者成功地實現跨學科信息傳遞。Steele 和 Stier (2000)對森林學的研究則發現跨學科的文章(意即其引用的參考文獻較多元)比集中在學科內的文章有較高的引用率，因此他們認為科學家會使用其他學科的文獻來增加其研究的影響力。宏觀層面的跨學科性分析主要處理學術領域之間的結構關係，例如引用流向(citation flow)提供科學地圖的圖形表示研究（例如參見Small，1999）或通過不同領域間學者的遷移(Hargens, 1986)。關於學科之間知識交換，先前的研究已經使用文獻中的跨學科引用進行分析(Van Leeuwen & Tijssen, 2000; Rinia et al., 2002; NSF, 2002)，這些研究都利用期刊分類的類別做為文章歸屬的子領域(subfields)。Rinia et al. (2002)提供了對領域之間關係的有趣洞察，並根據不同的跨學科性度量提出了領域排序系統(field ranking system)。

本研究中根據期刊分配到一個以上主題類別的特徵，也就是多重指派(Multi-assignation)，來檢驗科學的跨學科性，其根據的假設是指派為一個以上主題的期刊應該比單一指派的期刊更具跨學科性。本研究認為將期刊分配到多個類別表明學科之間存在認知聯繫，這可能進一步導致跨學科研究，並且可以追溯到一段時間的演變。以下是本研究的問題：首先，根據其跨學科性質描述類別，並建立不同類別的型態。其次，將研究領域(research areas)描述為它們類別的集合，探索這種自下而上的方法的可能性，並且以獲得領域的類型學為目的。最後，假定最近創建的類別由原先學科的專殊化(specialization)或混合而來，因此它們比較舊的類別更具跨學科性，根據期刊多重指派的指標，新增和增長最快的類別是否具有較高跨學科性。

本研究利用1996年ISI的SCI、SSCI和A&HCI資料庫上的8000種期刊，共有224個類別，每一個期刊至少被指派一個類別，最多則為五個類別。每一個類別包含的期刊，從4種到284種，分布相當偏斜(中位數為38，平均約為50)。期刊類別的指派是根據期刊的內容以及對其引用和被引用的模式進行分析所歸納出來的結果。本研究所使用的指標如下：
1. 每個類別的多重指派期刊的百分比，百分比愈高表示該類別的跨學科性愈高。
2. 多重指派為同一領域內的類別或為不同領域間的類別百分比。
3. 關係的多樣性(diversity of relationships)，由特定類別建立的不同連結的數量，即與此類別有共享期刊的不同類別的數量計算得到。
4. 兩個給定類別之間建立的關係的強度，類別A和類別B之間的關係的強度被計算為A和B共享的期刊數量除以A中的期刊數量的平方根乘以B中的期刊數量之間的比率，也就是類別A和類別B的Salton指標(Salton index, Salton & McGill, 1983)。

研究中，將區域和類別分群以獲得不同的型態，對出現的分群結果中眾所周知的跨學科類別的位置進行分析，做為最終型態的驗證標準。並且利用Noma (1986)對期刊研究層級的研究結果判斷各類別的基礎或應用科學。最後，分析了過去幾年ISI類別的演變情況，也利用多重指派指標對過去15年中最新加入ISI分類體系中的學科類別進行識別和描述，以檢驗新興學科具有更高跨學科性的假設。

平均上來說，每一個類別上有53%的期刊其主題為多重指派，看起來這個比例似乎相當高，但從知識結構的角度來看，似乎是合理的，因為學科的邊界是人為界定，不同學科的知識之間確實存在高度關係。在各類別上，期刊的多重指派比例有很大差異：人文領域的類別幾乎沒有多重指派的期刊，在某些較小的類別上，全部的期刊都是多重指派，較大並具有較高多重指派比例的類別包括生物心理學(Biological Psychology, 100%)、熱力學(Thermodynamics, 100%)、行為科學(Behavioral Sciences, 97%)、環境工程(Environmental Engineering, 96%)、儀器學(Instruments/Instrumentation, 96%)和醫學資訊學(Medical Informatics, 94%)等。

各領域的多重指派比例，以及多重指派為同一領域內的類別(internal)或為不同領域間的類別(external)的百分比如下



各領域期刊的多重指派比例，最高為Engineering/Technolgy(56.5%)和Biomedicine(56.4%)，最低則為Humanities(11.0%)，而且Humanities與其他領域差距很大。從期刊的多重指派比例來看，基礎科學(例如Biomedicine)和應用科學(例如Engineering/Technolgy)並沒有很大差異。

除了Social Sciences之外，其餘的領域多重指派為不同領域間的類別的比例較高，最多者為Chemistry、Physics和Mathematics，多重指派為同一領域內類別的比例較高者為Social Sciences、Humanities和Engineering/Technology。利用多重指派的比例和內/外部連結進行階層式叢集分析，其結果分為四群，Social Sciences與Humanities和其他領域有明顯不同，彼此也不相同，因此兩者各成一群。

平均而言，每個類別連結14個其他類別(平均為12，在1~49)之間，而且擁有期刊數愈多的類別，其連結愈多，例如：Biochemistry & Molecular Biology有284種期刊，共有49個連結；Environmental Sciences有120種期刊，共有46個連結； Psychology則有139 journals種期刊，有39個連結。在以期刊數調整後，各領域的連結分布，如下表所示：



以兩個類別之間的關係強度而言，具有較強關係的類別有Remote Sensing、Mathematical Psychology 、Physics/Particles & Fields以及Petroleum Engineering，其中Remote Sensing不但具有較強的關係強度，同時也具有較高的多樣性。下面是各領域具有的類別之間的關係強度：



根據各領域間的具有相同期刊的連結強度，利用多維尺度(multidimensional scaling)，呈現它們的相似性，



首先利用區辨分析(discriminant analysis)判別多重指派比例、外部多重指派比例、多樣性和連結強度等各種指標的重要性，結果只有具有低區辨力的多樣性被移除，然後根據其餘三種指標，對領域進行階層式集群分析(hierarchical cluster analysis)。結果如下：9個領域明現地分為兩群，Social Sciences和Humanities在一群，其餘的領域在另外一群。若是以更嚴格的標準來看，可以分為6群：第一群在各個指標上的結果都很高，可以說是最具有高跨領域性的領域，這群的成員包含Biomedicine和Engineering。第二群則有Physics和Chemistry，兩個領域都具有中高的多重指派比例和高外部多重指派比例，但只有中等的連結強度和多樣性。第三群包括Agriculture/Biology/Environmental Sciences和Clinical Medicine，四個指標都為中等程度。其餘三個領域各自成一群。Social Sciences有中等程度的跨領域性，但外部多重指派比例遠較內部多重指派比例來得低。Mathematics也是中等程度的跨領域性，但具有高外部多重指派比例以及低連結強度和多樣性。Humanities則是擁有最低的跨領域性。



進行類別的分群時，同樣利用區辨分析選擇多重指派的相關指標。在這個程序中，連結強度因為區辨力較低而被移除，因此根據其餘三種指標對219個類別(排除少於9種期刊的類別)進行K-平均(K-means)集群分析。在產生的結果中，第一群(cluster a)的57個類別具有最高的跨學科性，包括Thermodynamics、Biological Psychology以及Mathematical Psychology，許多類別有極高的連結多樣性，例如Biological Psychology連結其他10個類別，Medical Informatics則連結12個類別，在這群中許多類別直觀上便是跨學科，例如：Biochemical Research Methods、Biotechnology/Applied Microbiology、Environmental Sciences、Applied Physics、Applied Chemistry以及Medical Informatics等。根據多重指派比例，第二群(cluster b)的66個類別也具有較高的跨學科性，但它們的內部連結高於外部連結，而且多樣性和連結強度略低於第一群，內部連結較高顯示這些類別連結的其他類別大多屬於相同領域，例如Instrumentation連結的類別，與其同樣都屬於Engineering，Limnology連結其他的Agriculture領域下的類別，Transplantation則和Clinical Medicine領域的類別連結。第三群(cluster c)表現出中低水平的跨學科性，它們的多重指派比例較低，但是外部連結高於內部連結。這群共有55個類別，包括Psychology、Chemistry、Neurosciences以及Information Science/Library Science，Information Science雖然屬於Social Sciences領域，但和屬於Engineering領域Computer Science/Information System有關，所以它的外部連結比同屬於這一群的其他類別高。第四群(cluster d)的跨領域性最低，Humanities領域中約有70%的類別在這一群。

1981到1996年各領域中，以Engineering新增的期刊最多，平均每個類別增加154%，其次是Mathematics的134%，Physics的95%和Clinical Medicine的91%，低於平均值的有Biomedicine (50%)、Chemistry (57%)和Agriculture (75%)。

15年中新增的類別，共有38個，大部分是從舊的類別分離出來(例如Materials Science分離出Ceramics、Coverings、Biological Materials等)或是源自其他類別的混合(例如：Biotechnology、
Infectious Diseases、Transplantation)。擁有最多新增類別的領域是Engineering，共有21個。研究證實新增的類別比舊的有較高的跨領域性，它們在多重指派比例(69% 比55%)、連結強度(6.36比5.11)、連結多樣性(4.4 比3.4)等指標上都比較高。

因為那些被分配到多個類別的期刊包含對不同學科有用的知識，即跨學科知識，本研究便是利用此一概念，證實了利用多重指派相關指標做為跨領域性的有用性，其結果的解釋需要加以留意，因為本研究相當仰賴ISI的主題分類，而此一分類系統並非完美。另外，本研究對於本身便包含多種跨學科性期刊的一般類別的效果也並比較沒有效果。

本研究能夠建立各學科的型態，區分出連結的類別主要為其他領域的大跨學科性(big interdisciplinarity)以及主要為本身領域的小跨學科性(small interdisciplinarity)，並且也證實新出現的學科主要為大跨學科性，具有較高的跨學科性。

The bibliometric methodology presented here provides a general overview of all scientific disciplines, with special attention to their interrelation.

Interdisciplinarity is measured through a series of indicators based on Institute for Scientific Information (ISI) multi-assignation of journals in subject categories.

Research areas and categories are described according to the quantity of their links (number of related categories) and their quality (with close or distant categories, diversity, and strength of links).

This differentiates “big” interdisciplinarity, which links distant categories, from “small” interdisciplinarity, in which close categories are related.

The most commonly accepted definitions come from the OECD (1998), in which multi-disciplinarity, interdisciplinarity, and transdisciplinarity are used to refer to increasing levels of interaction among disciplines. Thus, in multidisciplinary research, the subject under study is approached from different angles, using different disciplinary perspectives and integration is not accomplished. Interdisciplinary research leads to the creation of a theoretical, conceptual, and methodological identity, so more coherent and integrated results are obtained. Finally, transdisciplinarity goes one step further and it refers to a process in which convergence among disciplines is observed, and it is accompanied by a mutual integration of disciplinary epistemologies (Van den Besselaar & Heimer-

The most commonly accepted definitions come from the OECD (1998), in which multi-disciplinarity, interdisciplinarity, and transdisciplinarity are used to refer to increasing levels of interaction among disciplines. Thus, in multidisciplinary research, the subject under study is approached from different angles, using different disciplinary perspectives and integration is not accomplished. Interdisciplinary research leads to the creation of a theoretical, conceptual, and methodological identity, so more coherent and integrated results are obtained. Finally, transdisciplinarity goes one step further and it refers to a process in which convergence among disciplines is observed, and it is accompanied by a mutual integration of disciplinary epistemologies (Van den Besselaar & Heimeriks, 2001).

Among other useful methods, we can mention those that analyze the collaboration between authors or centers from different disciplines (Qiu, 1992; Qin et al., 1997; Bordons etal., 1999), or those focused on the distribution of references/citations over categories (Porter & Chubin, 1985; Urata,1990; Tomov & Mutafov, 1996; Bourke & Butler, 1998; Van Leeuwen & Tijssen, 2000; Van Raan & Van Leeuwen, 2002; Rinia et al., 2002).

In these studies, disciplines are frequently operationalized in terms of journal sets. Thus,“boundary crossing” authors have been identified as those who publish in journals from disciplines outside their own.

The analysis of cross-disciplinary citations in journal articles has been used for the study of knowledge exchange between disciplines in previous studies (Van Leeuwen &Tijssen, 2000; Rinia et al., 2002; NSF, 2002), in which articles were attributed to subfields on the basis of the classification of journals into categories.

We consider that the multi-assignation of journals to more than one category indicates the existence of cognitive links between disciplines, which can result in interdisciplinary research and whose evolution can be traced over time.

In this study, we assume that the different disciplines will be differently involved in cross-disciplinary activities, as has been previously stated (OECD, 1998), and we would like to test the sensitivity of several journal multi-assignation indicators to discriminate between disciplines and areas ac-cording to their degree of interdisciplinarity.

The classificatory scheme of knowledge we have used is the classification of journals into subject categories of the Institute for Scientific Information (ISI) in the Science Citation Index (SCI), Social Sciences Citation Index (SSCI), and the Arts & Humanities Citation Index (A&HCI). This classificatory scheme, which is updated periodically, in 1996 grouped approximately 8,000 journals into 224 categories. Each journal was assigned to at least one scientific category, and multi-assignation in up to five categories was frequently allowed.

In our study, interdisciplinarity in science was examined through the assignation of journals to more than one subject category. We assume that those journals that appear under more than one subject heading should be more interdisciplinary than those single-assigned.

From this starting point, a set of indicators, based on multi-assignation, was introduced to quantify and qualify categories according to their interdisciplinary links:
a) Percentage of multi-assigned journals per category.
b) Multi-assignation pattern. It distinguishes “internal links,” which are the result of the multi-assignation of journals to categories of the same area, and “external links,” created by the multi-assignation of journals to categories of different areas.
c) Diversity of relationships, calculated as the number of different links established by a given category, that is, the number of different categories that share journals.
d) Strength/intensity of the relationships established between two given categories. The number of links between two categories is normalized according to the size of each of the categories by means of the Salton index (Salton & McGill, 1983).

In Table 1, we can observe for each area the following data: number of journals included, average percentage of multi-assigned journals, and multi-assignation pattern. The percentage of multi-assigned journals ranged from 11% in Humanities to 56% in Biomedicine and Engineering. The most isolated area according to this indicator was Humanities.

The areas most externally related were Chemistry, Mathematics, and Physics, whereas Social Sciences, Humanities, and Engineering showed the highest internal multi-assignation rate.

The areas were grouped according to their multi-assignation percentage and pattern through hierarchical clustering analysis and, thus, four different groups were obtained (see clusters A–D in the last column of Table 1). It is interesting to note that Humanities and Social Sciences remain separated and are not included in any group of areas, as they show a different behavioral pattern.

To quantify the diversity of relationships, the number of different links established between pairs of categories was calculated.

The strength of links between categories was analyzed through the Salton index. The higher the Salton index value between two given categories, the greater is their relationship or strength of links.

The research areas and the categories can be described through the combination of the different indicators introduced: multi-assignation percentage, external multi-assignation rate, diversity, and strength of links.

Areas were grouped according to the multi-assignation percentage, the percentage of external links, and the strength of links. The variable “diversity of links” was removed due to its low discriminating power found in the analysis.

The fact that a mainly basic area such as Biomedicine and the prototype of applied area that is Engineering/Technology are grouped together is surprising, and it indicates again that interdisciplinarity is not related to whether research is of the applied or basic type.

Those journals that are assigned to more than one category are to be read by different communities of scientists, so they must presumably include knowledge useful for different disciplines, that is, interdisciplinary knowledge.

Interdisciplinary indicators based on ISI multi-assignation of journals to categories has proved useful in providing a deeper understanding of the relations between disciplines. However, the results should be analyzed with caution since they are highly dependent on the ISI classification scheme, which is not perfect.

This approach allows the establishment of a typology of disciplines, which differentiates those responsible for the “big interdisciplinarity” (predominance of distant links, that is, those between different areas) and the “small interdisciplinarity” (links between close disciplines or disciplines of the same area).

New emerging disciplines are highly interdisciplinary, and show a predominance of the “big interdisciplinarity.”

In these studies, disciplines are

frequently operationalized in terms of journal sets. Thus,

“boundary crossing” authors have been identified as those

who publish in journals from disciplines outside their own.

In these studies, disciplines are

frequently operationalized in terms of journal sets. Thus,

“boundary crossing” authors have been identified as those

who publish in journals from disciplines outside their own.

iks, 2001).

The most commonly accepted

definitions come from the OECD (1998), in which multi-

disciplinarity, interdisciplinarity, and transdisciplinarity are

used to refer to increasing levels of interaction among

disciplines. Thus, in multidisciplinary research, the subject

under study is approached from different angles, using

different disciplinary perspectives and integration is not

accomplished. Interdisciplinary research leads to the cre-

ation of a theoretical, conceptual, and methodological iden-

tity, so more coherent and integrated results are obtained.

Finally, transdisciplinarity goes one step further and it refers

to a process in which convergence among disciplines is

observed, and it is accompanied by a mutual integration of

disciplinary epistemologies (Van den Besselaar & Heimer-

iks, 2001).

Wang, Q., & Waltman, L. (2016). Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus. Journal of Informetrics, 10(2), 347-364.

Wang, Q., & Waltman, L. (2016). Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus. Journal of Informetrics, 10(2), 347-364.

本研究以引用資料比較Web of Science和Scopus兩個資料庫提供的期刊分類系統(journal classification systems)的正確性。分類系統能應用於各種問題；例如，它可以被用來標定研究區域(Glänzel & Schubert, 2003; Waltman & Van Eck, 2012)，評估和比較研究對各領域的影響(Leydesdorff and Bornmann, 2015; Van Eck, Waltman, Van Raan, Klautz, & Peul, 2013)，以及跨學科的研究(Porter & Rafols, 2009; Porter, Roessner, & Heberger, 2008)。除了Web of Science和Scopus之外，期刊分類系統還有Science-Metrix、NSF(National Science Foundation)分類系統、the UCSD (University of California, San Diego)分類系統以及ANZSRC (Australian and New Zealand Standard Research Classification)，另外Glänzel and Schubert (2003)也提出一個包括期刊和論文的階層式分類系統，以演算法建構的期刊分類方法也有Bassecoulard and Zitt (1999)、Chen (2008)以及 Rafols and Leydesdorff (2009)等，Waltman and Van Eck (2012)的演算法則是以期刊裡出版的論文分類為主。

根據Waltman (2015, Section 3)的文獻分析，在比較Web of Science和Scopus時，主要針對資料庫的覆蓋情形(the coverage of the databases)，例如LópezIllescas, De Moya-Anegón, & Moed (2008)、 Meho & Rogers (2008)、 Mongeon & Paul-Hus (2016)、 Norris & Oppenheim (2007)，或是資料庫用來評估研究產生與影響的準確性，如Archambault, Campbell, Gingras, & Larivière (2009)、Bar-Ilan, Levene, & Lin (2007)、Meho & Rogers (2008)、Meho & Sugimoto (2009)，並未有研究比較與分析它們的分類系統的準確性。

過去Pudovkin and Garfield (2002)曾說明WoS首先利用人工的經驗法則將期刊分配到各類別，之後使用根據引用資料的Hayne-Coulson演算法對新的期刊進行分類。除此之外，Katz and Hicks (1995)、Leydesdorff (2007)、Leydesdorff and Rafols (2009)等研究也曾指出WoS的分類系統是綜合了引用模式、期刊題名與專家意見。但Scopus則未曾有文獻提到其分類系統的建構方式。事實上，在WoS上有兩個分類系統，一個為具有250個類別的類別系統(a system of categories)，另一個則是包含約150個研究領域的研究領域系統(a system of research areas)，此外，另一個分類系統僅包含科學與社會科學，稱為ESI (Essential Science Indicators)。本研究的分析對象是WoS上的類別系統。

Scopus的期刊分類系統則名為ASJC ( All Science Journal Classification)，分為兩個層級，下層有304個類別，上層則分為27個類別。

既然期刊分類系統相當有用，因此有許多研究提出對WoS及Scopus的分類系統進行改善的方法，例如Glänzel等人研究各種方法來驗證與改善WoS的分類系統 (Janssens, Zhang, De Moor, & Glänzel, 2009; Thijs, Zhang, & Glänzel, 2015; Zhang, Janssens, Liang, & Glänzel, 2010)，López-Illescas, Noyons, Visser, De Moya-Anegón, & Moed (2009) 則是對利用WoS分類系統進行的領域劃分，提出改進的方法。在Scopus分類系統的改進方面，則有SCImago團隊(Gómez-Núnez, ˜ Vargas-Quesada, De Moya-Anegón, & Glänzel, 2011, Gómez-Núnez, ˜ Batagelj, Vargas-Quesada, De Moya-Anegón, & Chinchilla-Rodríguez, 2014, Gómez-Núnez, ˜ Vargas-Quesada, & De Moya-Anegón, 2016)。

用來評估期刊分類系統準確性的方法可分為以專家為基礎的方法與書目計量方法(bibliometric approach)。以專家為基礎的方法在遭遇大量資料時有很大的困難，沒有專家有足夠知識來評估所有科學學科上的期刊分類，因此需要相當多的專家加入。書目計量方法可再分為以文本為基礎與以引用為基礎兩種方法，分別以在同一類別下期刊論文的文本相似性與引用模式的相似性大小做為衡量期刊是否應在同一類別的標準。本研究採用的是直接引用(direct citation)關係，先前Klavans and Boyack (2015)曾經利用直接引用關係建構論文的分類系統演算法，他們的結論認為直接引用較書目耦合(bibliographic coupling)或共被引(co-citation)等間接地引用關係更加準確。

綜合以上所述，本研究提出方法的原理可歸納為任一期刊引用該期刊所屬類別下的期刊或被這些期刊所引用的頻次必然較其他類別的期刊之頻次來得高。根據這樣基本原則，本研究制訂兩個檢驗期刊是否被指定於適當類別的標準：
標準1：某一期刊與它所屬類別的其他期刊之間若是只有相當少的引用關係，則這個期刊的分類可能有問題。
標準2：如果某一期刊與其他類別的期刊之間有相當多的引用關係，則這個期刊可能被分到不正確的類別下。

本研究以2010到2014年的WoS及Scopus資料庫上的所有期刊作為分析資料，相關的統計數據如表1所示，比較兩者所收錄的資料，Scopus資料庫除了比WoS更多的期刊種類與類別以外，每一種期刊被指定的類別數目也通常比較多，WoS上的期刊平均被指定到約1.6個類別，但是Scopus則為2.1。


根據第一項標準，WoS及Scopus兩個資料庫上都有許多期刊被指定到不適合的類別上，並且Scopus尤為嚴重，如表3所示。在各個至少有10種期刊的類別當中，選擇至少有一半的期刊符合標準一的類別，這樣的類別，WoS共有17個，Scopus則有高達76個，在兩個資料庫上都出現的類別包括建築學(ARCHITECTURE)、生物物理學(BIOPHYSICS)以及 醫學實驗室技術(MEDICAL LABORATORY TECHNOLOGY)。



兩個資料庫的情況在標準二上都有還不錯的結果(表6)。


同時符合標準一與標準二的期刊，一方面與本身被指定的類別只有較弱的連結，另一方面則與未被指定的類別有較強的連結，分析同時符合標準一與標準二的期刊可以發現，一種可能是在這些期刊上的發表已經和它們的題名和範圍宣告(scope statement)有所差異，另一種可能則是在分類時僅依賴它們的題名。

根據以上的實驗結果，可以歸納以下的幾點結論：
1. 在標準一，WoS的表現比Scopus還要好，因此可以說，Scopus上的期刊通常與它們被指定的類別只有較弱的連結。
2. 在標準二，兩個資料庫的表現都相當好，也就是如果某一期刊與某一個類別的連結較強的話，WoS及Scopus通常會將它指定到這個類別。
3. 整合兩個標準，WoS比Scopus的表現通常要好上許多。

除了上述的結論外，本研究還指出Scopus有些類別有容易混淆的名稱，例如有兩個類別分別命名為LINGUISTICS & LANGUAGE和LANGUAGE & LINGUISTICS，另兩個則為INFORMATION SYSTEMS & MANAGEMENT與MANAGEMENT INFORMATION SYSTEMS。

而且兩個分類系統都缺乏透明性，本研究的作者沒有發現建構與更新分類系統的適當文件。


To examine and compare the accuracy of journal classification systems, we define two criteria on the basis of direct citation relations between journals and categories. We use Criterion I to select journals that have weak connections with their assigned categories, and we use Criterion II to identify journals that are not assigned to categories with which they have strong connections. If a journal satisfies either of the two criteria, we conclude that its assignment to categories may be questionable.

Accordingly, we identify all journals with questionable classifications in Web of Science and Scopus. Furthermore, we perform a more in-depth analysis for the field of Library and Information Science to assess whether our proposed criteria are appropriate and whether they yield meaningful results.

It turns out that according to our citation-based criteria Web of Science performs significantly better than Scopus in terms of the accuracy of its journal classification system.

Classifying journals into research areas is an essential subject for bibliometric studies.

A classification system can assist with various problems; for instance, it can be used to demarcate research areas (e.g., Glänzel & Schubert, 2003; Waltman & Van Eck, 2012), to evaluate and compare the impact of research across scientific fields (e.g., Leydesdorff and Bornmann, 2015; Van Eck, Waltman, Van Raan, Klautz, & Peul, 2013), and to study the interdisciplinarity of research (e.g., Porter & Rafols, 2009; Porter, Roessner, & Heberger, 2008).

Besides the WoS and Scopus classification systems, there are various other multidisciplinary classification systems, for instance the system of Science-Metrix,the system of the National Science Foundation (NSF) in the US,the UCSD classification system, and the system of the Australian and New Zealand Standard Research Classification (ANZSRC).

Science-Metrix assigns “individual journals to single, mutually exclusive categories via a hybrid approach combining algorithmic methods and expert judgment” (Archambault, Beauchesne, & Caruso, 2011, p. 66). The Science-Metrix system includes 176 categories.

The NSF system also offers a mutually exclusive classification of journals, but it is more aggregated, consisting of only 125 categories (Boyack & Klavans, 2014). The system is used in the Science & Engineering Indicators of the NSF.

A more detailed classification system is the so-called University of California, San Diego (UCSD) classification system. This system, which includes more than 500 categories, has been constructed in a largely algorithmic way. The construction of the UCSD classification system is discussed by Börner et al. (2012).

The ANZSRC’s Field of Research (FoR) classification system has a three-level hierarchical structure. Journals are classified at the top level and at the intermediate level. Journals can have multiple classifications.

Furthermore, Glänzel and Schubert (2003) designed a two-level hierarchical classification system, which can be applied at the levels of both journals and publications. They adopted a top-bottom strategy; specifically, they first defined categories on the basis of the experience of bibliometric studies and external experts. They then assigned journals and individual publications to the categories. This classification system has for instance been used for measuring interdisciplinarity. In their analysis of interdisciplinarity, Wang, Thijs, & Glänzel (2015) explain that instead of the WoS subject categories they use the more aggregated classification system developed by Glänzel and Schubert (2003).

Algorithmic approaches to construct classification systems at the level of journals have been studied by for instance Bassecoulard and Zitt (1999), Chen (2008), and Rafols and Leydesdorff (2009).

A more recent development is the algorithmic construction of classification systems at the level of individual publications rather than journals. Waltman and Van Eck (2012) developed a methodology for algorithmically constructing classification systems at the level of individual publications on the basis of citation relations between publications. Their approach has for instance been used in the calculation of field-normalized citation impact indicators (Ruiz-Castillo & Waltman, 2015).

According to a recent literature review (Waltman, 2015, Section 3), previous studies comparing WoS and Scopus are mainly focused on two aspects. One is the coverage of the databases (e.g., LópezIllescas, De Moya-Anegón, & Moed, 2008; Meho & Rogers, 2008; Mongeon & Paul-Hus, 2016; Norris & Oppenheim, 2007) and the other is the accuracy of the databases when used to assess research output and impact at different levels, ranging from individual researchers to departments, institutes, and countries (e.g., Archambault, Campbell, Gingras, & Larivière, 2009; Bar-Ilan, Levene, & Lin, 2007; Meho & Rogers, 2008; Meho & Sugimoto, 2009). However, no study has systematically compared WoS and Scopus in terms of the accuracy of their journal classification systems.

In the case of WoS, Pudovkin and Garfield (2002) have offered a brief description of the way in which categories are constructed. According to Pudovkin and Garfield, when WoS was established, a heuristic and manual method was adopted to assign journals to categories, and after this, the so-called Hayne-Coulson algorithm was used to assign new journals. This algorithm is based on a combination of cited and citing data, but it has never been published.

Besides this, Katz and Hicks (1995), Leydesdorff (2007), and Leydesdorff and Rafols (2009) have indicated that the WoS classification system is based on a comprehensive consideration of citation patterns, titles of journals, and expert opinion.

In the case of Scopus, there seems to be no information at all on the construction of its classification system.

It should be mentioned that in the most recent versions of WoS two classification systems are available, namely a system of categories and a system of research areas.

The system of categories is more detailed. This system, which is the traditional classification system of WoS and the system on which we focus our attention in this paper, consists of around 250 categories and covers the sciences, social sciences, and arts and humanities.

The system of research areas, which has become available in WoS more recently, is less detailed and comprises around 150 areas.

Besides these two systems, Thomson Reuters also has a classification system for its Essential Science Indicators. This system consists of 22 subject areas in the sciences and social sciences. It does not cover the arts and humanities.

The Scopus journal classification system is called the All Science Journal Classification (ASJC). It consists of two levels. The bottom level has 304 categories, which is somewhat more than the about 250 categories in the WoS classification system. The top level includes 27 categories.

The accuracy of a classification system can seriously influence bibliometric studies. For instance, Leydesdorff and Bornmann (2015) investigated the use of the WoS categories for calculating field-normalized citation impact indicators. They focused specifically on two research areas, namely Library and Information Science and Science and Technology Studies. Their conclusion is that “normalizations using (the WoS) categories might seriously harm the quality of the evaluation”.

A similar conclusion was reached by Van Eck et al. (2013) in a study of the use of the WoS categories for calculating field-normalized citation impact indicators in medical research areas.

Glänzel and colleagues have studied several approaches to validate and improve WoS-based classification systems (Janssens, Zhang, De Moor, & Glänzel, 2009; Thijs, Zhang, & Glänzel, 2015; Zhang, Janssens, Liang, & Glänzel, 2010). They have also proposed an improved way of handling publications in multidisciplinary journals (Glänzel, Schubert, & Czerwon, 1999;Glänzel, Schubert, Schoepflin, & Czerwon, 1999).

Related to this, López-Illescas, Noyons, Visser, De Moya-Anegón, & Moed (2009) have studied an approach to improve the field delineation provided by categories in the WoS classification system.

The SCImago research group has made a number of attempts to improve the Scopus classification system (Gómez-Núnez, ˜ Vargas-Quesada, De Moya-Anegón, & Glänzel, 2011, Gómez-Núnez, ˜ Batagelj, Vargas-Quesada, De Moya-Anegón, & Chinchilla-Rodríguez, 2014, Gómez-Núnez, ˜ Vargas-Quesada, & De Moya-Anegón, 2016).

Two types of approaches can be distinguished for assessing the accuracy of journal classification systems. One is the expert-based approach and the other is the bibliometric approach.

Applying the expert-based approach at a large scale is challenging. No expert has sufficient knowledge to assess the classification of journals in all scientific disciplines, so a large number of experts would need to be involved.

In the case of the bibliometric approach, a further distinction can be made between text-based and citation-based approaches.

Text-based approaches could for instance assess whether the textual similarity of publications in journals assigned to the same category is higher than the textual similarity of publications in journals assigned to different categories.

Instead, we take a citation-based approach to assess the accuracy of journal classification systems.

In this paper, we use direct citation relations. This is because “a co-citation or bibliographic coupling relation requires two direct citation relations” (Waltman & Van Eck, 2012, p. 2380), which means that bibliographic coupling and co-citation relations are more indirect signals of the relatedness of journals than direct citation relations.

The use of direct citation relations is also supported by Klavans and Boyack (2015), who study the algorithmic construction of classification systems at the level of individual publications. They conclude that the use of direct citation relations yields more accurate results than the use of bibliographic coupling or co-citation relations.

Thus, the rationale of our approach can be summarized as follows: A journal should cite or be cited by journals within its own category with a high frequency in comparison with journals outside its category.

Based on this basic principle, we define two criteria to identify journals with questionable classifications. One criterion is that if a journal has only a very small number of citation relations with other journals within its own category, then we believe the classification of the journal to be questionable. The other criterion is that if a journal has many citation relations with journals in a category to which the journal itself does not belong, then it seems likely that the journal incorrectly has not been assigned to this category.

We retrieved from the WoS and Scopus databases all journals that have publications between 2010 and 2014....The choice of a five-year time window is a trade-off between on the one hand the stability of journal classification systems and on the other hand the accuracy of our approach based on direct citation relations.

As can be seen in Table 1, the number of Scopus journals included in the analysis is almost twice as large as the number of WoS journals, and Scopus also includes 80 more categories than WoS. Furthermore, although both databases often assign journals to multiple categories, we found that Scopus tends to assign journals to more categories than WoS. WoS assigns journals to at most six categories, whereas in Scopus there turns out to be a journal that is assigned to 27 categories. Additionally, we found that the average number of categories to which journals belong equals 1.6 in WoS and 2.1 in Scopus. This shows that on average journals have significantly more category assignments in Scopus than in WoS.

As can be seen, almost 60% of all journals in WoS belong to only one category, whereas in Scopus more than 60% of all journals are assigned to two or more categories.


WoS has 1390 journals with ti < 100, accounting for 11% of the total number of WoS journals, whereas Scopus has 5808 journals with ti < 100, which is 24% of the total.3 Hence, Scopus has more journals with ti < 100 than WoS not only in an absolute sense but also from a relative point of view.

Taking a further look at Scopus journals with ti < 100, it turns out that they can be roughly divided into three groups. One group consists of arts and humanities journals, another group consists of newly included journals, and a third group consists of non-English language journals.

Table 2 provides some basic statistics on the assignment of journals to categories in WoS and Scopus when journals with ti < 100 and assignments of journals to multidisciplinary categories are excluded. The table shows the number of journals that belong to at least one non-multidisciplinary category and the number of assignments of journals to non-multidisciplinary categories. As can be seen in the table, in the case of Scopus the constraints that we have introduced cause a much larger decrease in the number of journals and the number of journal-category assignments than in the case of WoS.

Table 3 reports for both WoS and Scopus and for three values of the threshold˛the number of journals and the number of journal-category assignments that satisfy Criterion I.

As can be seen, both databases have assigned a significant number of journals to categories that according to Criterion I seem to be inappropriate.

Moreover, no matter which threshold is considered, Scopus performs substantially worse than WoS, not only in the absolute number of journals and journal-category assignments satisfying Criterion I but, more importantly, also in the percentage of journals and journal-category assignments satisfying the criterion.

Next, we identify WoS and Scopus categories with a high percentage of journals satisfying Criterion I. The identified categories may be seen as the most problematic categories in the two databases, because many of the journals belonging to these categories are only weakly connected to each other in terms of citations.

We select categories that include at least 10 journals with ti ≥ 100 and that, for α˛= 0.1, have at least 50% of their journals satisfying Criterion I. The results for WoS and Scopus are reported in Tables 4 and 5, respectively. In the case of WoS 17 categories have been identified, whereas in the case of Scopus 76 categories have been identified, so more than four times as many as in the case of WoS.

There are three categories that have been identified in the case of both databases: ARCHITECTURE, BIOPHYSICS, and MEDICAL LABORATORY TECHNOLOGY.

Table 6 presents for both WoS and Scopus and for five values of the threshold ˇ the number of journals that satisfy Criterion II.

A journal satisfies both Criterion I and Criterion II if on the one hand it has weak connections, in terms of citations, with its assigned categories while on the other hand it has a strong connection with a category to which it is not assigned. More precisely, our focus is on journals for which the current category assignments all satisfy Criterion I, while there is an alternative category assignment that satisfies Criterion II.

Based on the three journals discussed above, we conclude that journals satisfying the combined Criteria I and II can be classified into at least two types. One type refers to journals for which there is a discrepancy between on the one hand their title and their scope statement and on the other hand what they have actually published.  ... The second type refers to journals that seem to have been assigned to a category based only on their title.

First, WoS performs much better than Scopus according to Criterion I. Using the parameter values ˛= 0.05 and ˛= 0.1, the percentage of journals and journal-category assignments satisfying Criterion I is more than two times higher for Scopus than for WoS. Hence, in Scopus journals are assigned to categories with which they are only weakly connected much more frequently than in WoS.

Second, based on Criterion II, WoS and Scopus both perform reasonably well, with WoS having a somewhat better performance than Scopus. For all parameter values that were considered, less than 5% of all journals in WoS and Scopus satisfy Criterion II. In other words, if a journal is strongly connected to a category, WoS and Scopus typically assign the journal to that category.

Third, WoS also presents a significantly better result than Scopus based on the combined Criteria I and II. In WoS there is only one journal satisfying the combined criteria, whereas in Scopus there are 32.

First, Scopus sometimes has confusing category labels. In particular, Scopus sometimes has two categories with very similar labels. Examples are the categories LINGUISTICS & LANGUAGE and LANGUAGE & LINGUISTICS and the categories INFORMATION SYSTEMS & MANAGEMENT and MANAGEMENT INFORMATION SYSTEMS.

Second, lack of transparency is a weakness of both the WoS and the Scopus classification system. We did not find proper documentation of the methods used to construct and update the WoS and Scopus classification systems.

For instance, in the case of a small category, it may be hardly possible for a journal to have a reasonably high relatedness with the category. Therefore it can be expected that many journals belonging to the category will satisfy Criterion I. This may be caused not so much by the misclassification of these journals but more by the small size of the category. On the other hand, in the case of a large category, there may be other problems. A large category may for instance be of a heterogeneous nature and may cover multiple fields that are hardly connected to each other.