Scientometrics
本研究
科學專業可視為對相同研究問題、使用相同方法並且參考相同的科學文獻來進行工作的人所組成的網路。在同一個專業裡的研究者彼此間的傳播要比和其他社群的人來得多，並且參考彼此的研究工作也比專業外的人的研究工作明顯來得多。因此科學專業與學科可以視為是傳播網路，在此一網路內的傳播比和其他專業研究者的傳播強度大。過去的研究已顯示期刊間的引用可以做為科學的學科組織的操作性指標，屬於同一學科內的期刊的引用關係比和其他期刊更強。在利用期刊之間的引用關係的研究上，可以從關連性分析(relational analysis)，確認彼此間有強連結的節點所形成的派系(cliques)，另一種方式則是根據在網路上有相同位置的節點，來發現網路的結構。
本研究採用的方式是利用因素分析(factor analysis)技術對使用期刊間的引用，進行結構式分析，根據期刊集合的逐漸穩定與一致情形，分析人工智慧(Artificial Intelligence, AI)領域在1982到1992年之間的發展。此一分析從選擇Artificial Intelligence做為核心期刊開始，根據Artificial Intelligence引用其他期刊與被其他期刊引用的情形建立關連環境(relational environment)，進行因素分析，關注傳播網路的結構改變。將Artificial Intelligence有最高因素負荷(the highest factor-loading)的因素做為這個專業的代表，如果選擇的Artificial Intelligence愈是在專業的中心，所得到的分析描述將會愈可信，其它的因素則做為相關的專業。
研究結果發現1982到1988年間，期刊集合不僅相當異質的期刊，同時每段時期的變化也很大，幾乎沒有重複，可見得在80年代初期AI並不能算是一個成熟的專業，然而從1988年起，三、四種期刊穩定地成為AI的核心，此外從期刊標題(journal titles)明顯的相關，可以知道整個期刊集合的凝聚力相當高。
We use aggregated journal-journal citations for describing Artificial Intelligence as sets of journals; factor analytic techniques are used to analyze the development of AI in terms of (an emerging) stability and coherency of the journal-sets during the period 1982-1992.
A scientific specialty can be defined as a network of people working on the same set of research questions, using the same methods and referring to the same scientific literature (see, e.g., Kuhn 1962; Price 1965; cf. Chubin, 1983).
Researchers within a specialty communicate more with one another than with researchers in other communities, and they are expected to refer to one another’s work significantly more frequently than to the work of outsiders.
Scientific specialties and disciplines can thus be considered as communication-networks (e.g., Griffith & Mullins, 1972; Shrum and Mullins, 1988). The communication within such a network is much more intensive than the communication with researchers in other specialties (e.g., McCain, 1990).
In a number of studies, it has been shown that journal-journal citations can be used as an operational indicator for the disciplinary organization of the sciences (e.g., Doreian & Fararo, 1985; Borgman & Rice, 1992; Tijssen, 1992; Cozzens & LeydesdorE, 1993). One may expect strong citation relations within and between journals belonging to a discipline, and (much) weaker relations with other journals.
In general. one can analyze communication networks in two different ways: First, the links between the nodes in the network can be analyzed in terms of graphs. In such a relational analysis, the nodes with strong (mutual) links are considered as so called “cliques.” Second, the latent structure of the network can be studied in terms of so called “eigen-vectors” (cf. Studer & Chubin, 1980). This structural analysis reveals which nodes have similar positions in the network.
In this study, we use citation relations among journals for the delination of the relevant domains, but we use the positional or structural approach for the analysis of development patterns.
We use factor-analysis for detecting the structural characteristics of each communication network. Journals that belong to the same specialty are expected to have a similar position within this communication system, that is, resemble each other in terms of their citation behavior. Thus, we obtain an operational definition of scientific specialties, which can be investigated in terms of coherency (in each year), stability and change (over time).
If the journal-set remains relatively stable over time, this suggests that the specialty is more mature and established.
Finally, a major scientific development like a change of paradigms, may be indicated by a radical rearrangement of the relevant journal-set in relation to other subsets. In other words, the question whether a field of research is becoming a coherent specialty or not can be reformulated as the question whether the field under study can be represented by a stable set of journals.
Our methods provide us with criteria to distinguish between gradual change in terms of journals emerging within existing structural units, and structural change in terms of change at the level of groups of journals (cf. Leydesdorff, Cozzens, & Van den Besselaar, 1994).
The analysis begins with a relational analysis of a core journal of the field under study. The entrance journal determines the domain for the analysis. Therefore, the results rely heavily on this choice: The more central this journal is in the journal-set representing the specialty, the more reliable the resulting delineations of the groupings are expected to be (Leydesdorff & Cozzens, 1993).
The matrices which represent the relational communication network of Artificial Intelligence, are factor-analyzed, both in the “citing” and in the “cited” dimension.
The “citing” dimension refers to the aggregated citing behavior of authors who publish in these journals in the current year. We interpret this as the active reproduction of the structure of the specialty. The authors involved have an opportunity to contribute to a change of the position of the journal in which they publish, by relating to other literatures.
The potential change in position is beyond the control of an individual author, and thus we are looking at structural change and not at behavioral intentions (Leydesdorff, 1993, 1995 ) .
In the “cited” dimension, on the other hand, the history of the journal is reflected, and therefore, the “cited” dimension reflects the codification into the archive of the scientific literature.
Thus, the latter is expected to develop more slowly than the “citing” dimension, and citations may have different meanings in different contexts (Small, 1978; cf. Leydesdorff & Amsterdamska, 1990).
The factor on which the entrance journal (i.e., Artificial Intelligence) has the highest factor-loading, is considered as representing the specialty which we try to delineate (i.e., “artificial intelligence”). The other factors that result from the analysis can partly be interpreted as specialties that are relevant for, and related to, the focal specialty.
Between 1982 and 1988, the journal-sets in Table 3 are, apart from the entrance journal, even completely disjointed. ... These sets do not only change, they also consist of heterogeneous journals. Using this criterion, AI cannot be considered as a mature specialty in the first part of the eighties.
However, starting in 1988, a set of three or four journals seems to become a stable core of the AI-cluster. ... Additionally, the journal-set becomes more coherent, in the sense that the journal titles are substantively related.
Table 4 exhibits the factor designations in the relational citation environment of Artificial Intelligence in each year. Each factor is interpreted as representing a scientific specialty. Which specialty, is determined by the nature of the subjects of the journals that have their highest loading on this factor.
本研究
科學專業可視為對相同研究問題、使用相同方法並且參考相同的科學文獻來進行工作的人所組成的網路。在同一個專業裡的研究者彼此間的傳播要比和其他社群的人來得多，並且參考彼此的研究工作也比專業外的人的研究工作明顯來得多。因此科學專業與學科可以視為是傳播網路，在此一網路內的傳播比和其他專業研究者的傳播強度大。過去的研究已顯示期刊間的引用可以做為科學的學科組織的操作性指標，屬於同一學科內的期刊的引用關係比和其他期刊更強。在利用期刊之間的引用關係的研究上，可以從關連性分析(relational analysis)，確認彼此間有強連結的節點所形成的派系(cliques)，另一種方式則是根據在網路上有相同位置的節點，來發現網路的結構。
本研究採用的方式是利用因素分析(factor analysis)技術對使用期刊間的引用，進行結構式分析，根據期刊集合的逐漸穩定與一致情形，分析人工智慧(Artificial Intelligence, AI)領域在1982到1992年之間的發展。此一分析從選擇Artificial Intelligence做為核心期刊開始，根據Artificial Intelligence引用其他期刊與被其他期刊引用的情形建立關連環境(relational environment)，進行因素分析，關注傳播網路的結構改變。將Artificial Intelligence有最高因素負荷(the highest factor-loading)的因素做為這個專業的代表，如果選擇的Artificial Intelligence愈是在專業的中心，所得到的分析描述將會愈可信，其它的因素則做為相關的專業。
研究結果發現1982到1988年間，期刊集合不僅相當異質的期刊，同時每段時期的變化也很大，幾乎沒有重複，可見得在80年代初期AI並不能算是一個成熟的專業，然而從1988年起，三、四種期刊穩定地成為AI的核心，此外從期刊標題(journal titles)明顯的相關，可以知道整個期刊集合的凝聚力相當高。
We use aggregated journal-journal citations for describing Artificial Intelligence as sets of journals; factor analytic techniques are used to analyze the development of AI in terms of (an emerging) stability and coherency of the journal-sets during the period 1982-1992.
A scientific specialty can be defined as a network of people working on the same set of research questions, using the same methods and referring to the same scientific literature (see, e.g., Kuhn 1962; Price 1965; cf. Chubin, 1983).
Researchers within a specialty communicate more with one another than with researchers in other communities, and they are expected to refer to one another’s work significantly more frequently than to the work of outsiders.
Scientific specialties and disciplines can thus be considered as communication-networks (e.g., Griffith & Mullins, 1972; Shrum and Mullins, 1988). The communication within such a network is much more intensive than the communication with researchers in other specialties (e.g., McCain, 1990).
In a number of studies, it has been shown that journal-journal citations can be used as an operational indicator for the disciplinary organization of the sciences (e.g., Doreian & Fararo, 1985; Borgman & Rice, 1992; Tijssen, 1992; Cozzens & LeydesdorE, 1993). One may expect strong citation relations within and between journals belonging to a discipline, and (much) weaker relations with other journals.
In general. one can analyze communication networks in two different ways: First, the links between the nodes in the network can be analyzed in terms of graphs. In such a relational analysis, the nodes with strong (mutual) links are considered as so called “cliques.” Second, the latent structure of the network can be studied in terms of so called “eigen-vectors” (cf. Studer & Chubin, 1980). This structural analysis reveals which nodes have similar positions in the network.
In this study, we use citation relations among journals for the delination of the relevant domains, but we use the positional or structural approach for the analysis of development patterns.
We use factor-analysis for detecting the structural characteristics of each communication network. Journals that belong to the same specialty are expected to have a similar position within this communication system, that is, resemble each other in terms of their citation behavior. Thus, we obtain an operational definition of scientific specialties, which can be investigated in terms of coherency (in each year), stability and change (over time).
If the journal-set remains relatively stable over time, this suggests that the specialty is more mature and established.
Finally, a major scientific development like a change of paradigms, may be indicated by a radical rearrangement of the relevant journal-set in relation to other subsets. In other words, the question whether a field of research is becoming a coherent specialty or not can be reformulated as the question whether the field under study can be represented by a stable set of journals.
Our methods provide us with criteria to distinguish between gradual change in terms of journals emerging within existing structural units, and structural change in terms of change at the level of groups of journals (cf. Leydesdorff, Cozzens, & Van den Besselaar, 1994).
The analysis begins with a relational analysis of a core journal of the field under study. The entrance journal determines the domain for the analysis. Therefore, the results rely heavily on this choice: The more central this journal is in the journal-set representing the specialty, the more reliable the resulting delineations of the groupings are expected to be (Leydesdorff & Cozzens, 1993).
The matrices which represent the relational communication network of Artificial Intelligence, are factor-analyzed, both in the “citing” and in the “cited” dimension.
The “citing” dimension refers to the aggregated citing behavior of authors who publish in these journals in the current year. We interpret this as the active reproduction of the structure of the specialty. The authors involved have an opportunity to contribute to a change of the position of the journal in which they publish, by relating to other literatures.
The potential change in position is beyond the control of an individual author, and thus we are looking at structural change and not at behavioral intentions (Leydesdorff, 1993, 1995 ) .
In the “cited” dimension, on the other hand, the history of the journal is reflected, and therefore, the “cited” dimension reflects the codification into the archive of the scientific literature.
Thus, the latter is expected to develop more slowly than the “citing” dimension, and citations may have different meanings in different contexts (Small, 1978; cf. Leydesdorff & Amsterdamska, 1990).
The factor on which the entrance journal (i.e., Artificial Intelligence) has the highest factor-loading, is considered as representing the specialty which we try to delineate (i.e., “artificial intelligence”). The other factors that result from the analysis can partly be interpreted as specialties that are relevant for, and related to, the focal specialty.
However, starting in 1988, a set of three or four journals seems to become a stable core of the AI-cluster. ... Additionally, the journal-set becomes more coherent, in the sense that the journal titles are substantively related.
Our analysis suggests that the subject matter, as well as the methods of AI are gradually becoming more focused and accepted in a specific community of researchers. AI is increasingly covering a delineatable field, and the early speculative claims of AI now seem to be translated into more “normal problem solving” activities. This may support the view that AI is entering a “paradigmatic phase.”
Table 4 exhibits the factor designations in the relational citation environment of Artificial Intelligence in each year. Each factor is interpreted as representing a scientific specialty. Which specialty, is determined by the nature of the subjects of the journals that have their highest loading on this factor.
Among the different specialties the “pattern analysis” factor is most prominently present in all years. It is also rather stable in its composition.
Additionally, one can see in most years a “theoretical computer science” factor, and an “applied computer science” factor, consisting of journals on information systems and software engineering.
The factor on man-machine studies is visible in all years except 1992. In this year, the International Journal of Man-Machine Studies loads highest on the AI-factor.
“Cognitive psychology” and “cognitive science” are in the environment of AI in most of the years, but in 1982 and 1988 the cognitive science journals and psychology journals are part of the AI-factor itself.
The predominance of “computer science” clusters in the environment of AI contradicts the view of AI as an (interdisciplinary) set of specialties, all studying intelligence and computational models of intelligence. This result corresponds to the conclusion in an article of Khawarn ( 1991) who, using a different methodology, demonstrated that AI is not an interdisciplinary field, but mainly related to (computer) science.
Although in every year we see some of these specialties, in the more recent years they are more numerous. This observation suggests that AI-techniques are more and more used in other specialties. The opinions of Schank and Winograd (in Bobrow & Hayes, 1985) that AI might be mainly a methodology, are supported by this result.
In this article, we defined scientific specialties as communication networks that can be delineated through the analysis of aggregated journal-journal citations. We used a method for mapping the development of scientific specialties and of their respective relations with other specialties over time.
The journal cluster of AI is unstable in a changing environment. But there seems to have been a change in the structure of this communication network between 1986 and 1988.
Before this date, AI has not been an “interreading community of scientists”; AI was presumably in a preparadigmatic phase at the time.
However, in the second part of the decade, research in AI seems to be more focused, and with hindsight this indicates that AI is entering a new phase. The structure of the field becomes more pronounced and stable: The AI-cluster becomes clearly delineatable, and this delineation is stabilized after 1988.
Furthermore, the factor analytical approach (as contrasted with clustering techniques) allowed us to consider journals to be part of more than one journal-set, and therefore it enabled us to study these specialties during phases of emergence and structural change, and within the context of already more established specialties.
In other words, our results do not support Boden’s view that AI has remained a relatively unstructured field of philosophical questions. First, AI has since then become more focused, and both expert systems and the more theoretical aspects of AI are increasingly represented in this core. Expert systems research has become a structural element of AI-research. Robotics has also become more closely related to AI during the later part of the 1980s. Thus, our results contradict the prediction that relatively successful subfields of AI are systematically institutionalized as separate specialties.
On the other hand, Sloman’s view of AI as cognitive science is also not supported by our results. Journals with “cognitive science” in the title appear in the AI-cluster from time to time. These journals, however, have mainly bridging functions with specialties in the environment of AI. More often, psychology and cognitive science play a role as separate specialties in the citation environment of AI.
The view that AI itself is a collection of specialties, all studying “intelligence,” is also not supported by our data. Many of the specialties that study intelligence, like philosophy, linguistics, neural sciences. cultural sciences, are hardly visible in the communication network surrounding AI. Only occasionally. we see isolated philosophical or linguistic journals having citation relations with AI-journals.
The specialties that are visible in the environment of AI in all or most years are pattern analysis, computer science, and cognitive psychology. As noted, the presence of robotics is becoming more pronounced. The view of Schank and Winograd, i.e., that AI is mainly a methodology. therefore seems to be supported by our indicators. Notably in more recent years, the number of specialties that use AI-techniques increases significantly: Biology, medicine, operations research, statistics, geology, chemistry. etc.
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