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The Communication of Ideas across Subfields in Political Science

Published online by Cambridge University Press:  14 April 2014

Erin R. Graham
Affiliation:
Drexel University
Charles R. Shipan
Affiliation:
University of Michigan
Craig Volden
Affiliation:
University of Virginia
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Abstract

What factors inhibit or facilitate cross-subfield conversations in political science? This article draws on diffusion scholarship to gain insight into cross-subfield communication. Diffusion scholarship represents a case where such communication might be expected, given that similar diffusion processes are analyzed in American politics, comparative politics, and international relations. We identify nearly 800 journal articles published on diffusion within political science between 1958 and 2008. Using network analysis we investigate the degree to which three “common culprits”—terminology, methodological approach, and journal type—influence levels of integration. We find the highest levels of integration among scholars using similar terms to describe diffusion processes, sharing a methodological approach (especially in quantitative scholarship), and publishing in a common set of subfield journals. These findings shed light on when cross-subfield communication is likely to occur with ease and when barriers may prove prohibitive.

Type
The Profession
Copyright
Copyright © American Political Science Association 2014 

Political science often has been characterized as a fragmented discipline (Almond Reference Almond1988; Garand Reference Garand2005; Sigelman Reference Sigelman2006). As a group, political scientists do not subscribe to a set of core assumptions or a single epistemological stance, and the discipline’s empirical pursuits are often divided—perhaps too neatly—into subfields of American politics (AP), comparative politics (CP), and international relations (IR). These subfields organize the professional lives of political scientists; as Bennett and Elman (2007, 112) note, “journals are often delineated along subfield lines, as are the courses faculty teach and all too often the literatures they read.” Furthermore, divisions exist within these subfields, often demarcated by methodological approach and resulting in political scientists sitting at “separate tables” (Almond Reference Almond1988). Indeed, paradigmatic debates in IR (Katzenstein, Keohane, and Krasner Reference Katzenstein, Keohane and Krasner1998), regionalization in CP (Caporaso Reference Caporaso2000), and the marginalization of qualitative methods in AP (Pierson Reference Pierson2007) have carved out subfields within subfields in political science.

Given this fragmented nature, advocates of a question-driven discipline might wonder whether scholars investigating common questions are capable of sustained dialogue that crosses subfield, methodological, and other boundaries. The question is timely: scholars in different subfields are often and increasingly engaged in similar intellectual pursuits. At a broad level, AP and CP scholars study similar aspects of political behavior and institutions in domestic settings, while CP and IR scholars share an interest in the spread of democracy, the causes of ethnic conflict, and the domestic sources of international trade policy, to name but a few common topics.

Yet, having sat at different tables for so long—viewing topics through subfield lenses, using different methods and terminologies, and reading different journals—are political scientists able to communicate with one another across subfield boundaries? We address this question by examining the state of cross-subfield relations in diffusion scholarship. A dizzying array of topics of inquiry in political science—including war, democracy, norms, ideas, preferences, economic and social conditions, and policies—are all subject to diffusion processes. Indeed, diffusion scholarship has rapidly advanced in all three subfields of empirical political science (Graham, Shipan, and Volden Reference Graham, Shipan and Volden2013). This growth makes the analysis of diffusion scholarship timely, but it also makes this topic especially ripe for analyzing cross-subfield scholarship. Where similar topics are being studied in different subfields, as in scholarship on diffusion, we would expect acknowledgment of advances and conversations beyond subfield lines. Does that occur?

In this article we identify and then draw on nearly 800 articles published on diffusion within political science between 1958 and 2008 and develop expectations regarding when we should see connections made across subfields. We then conduct a network analysis to illustrate those connections, based on citation patterns across these articles. Not surprisingly, very few connections exist between some topics—for example, the study of conflict diffusion in IR and the study of policy diffusion in AP. But even when we restrict our analysis to similar topics—the diffusion of policies, which is examined within all subfields, as well the CP and IR scholarship on the diffusion of norms—we find an uneven level of integration.

To better understand why there are more engaged conversations across subfields in some areas than in others, we then explore three main “culprits” that may have reinforced subfield divides. Specifically, we examine the differences in terminology, methodology, and journal placement across these diffusion studies to determine whether each is associated with barriers to cross-subfield communication. We find the most robust citation networks (1) within studies using common terms to describe diffusion processes, (2) within scholarship using similar methodological approaches (especially among quantitative studies), and (3) within each subfield’s own journals. Such findings highlight why subfield barriers are lower for some sets of scholarly works, while remaining perhaps insurmountably high elsewhere.

DIFFUSION STUDIES IN POLITICAL SCIENCE

We begin with a broad definition: diffusion occurs when one government’s choices are influenced by the choices of other governments. This definition encompasses a wide range of topics, including the spread of conflict across borders, socialization and the spread of norms facilitated by international organizations, and the spread of specific policies or governmental structures across polities, to name some of the most common categories. Using a broad set of search terms, we built a database of all 781 diffusion articles published between 1958 and 2008 in 53 top political science journals.Footnote 1

We then coded each article using a number of criteria, including the subfield of study and “what is diffusing”—that is, the feature of politics that is diffusing (e.g., conflict, norms, and policies). To identify the subfield of study, we categorized each article as AP, CP, IR, or other, based on the subject matter, the author’s main subfield affiliation, and the journal in which the article was published. Two of us separately categorized each article, and the third author resolved any differences. For “what is diffusing,” the largest category by far was “policy,” with substantial numbers of studies on norms or ideas, conflict, democracy or other forms of government, innovations (such as technology), and conditions (such as convergence of economic or environmental outcomes).

We then collected information on which of these diffusion articles cited one another, thereby forming the basis for a network analysis. The details of our qualitative and quantitative assessments of this literature are offered elsewhere (Graham, Shipan, and Volden Reference Graham, Shipan and Volden2013; see also Shipan and Volden Reference Shipan and Volden2012). For our purposes here, we illustrate the connections of these studies across pairs of political science subfields in figures 1figures –figures 3.Footnote 2

Figure 1 Diffusion Studies in American and Comparative Politics

Figure 2 Diffusion Studies in American Politics and International Relations

Figure 3 Diffusion Studies in Comparative Politics and International Relations

Figure 1, which includes articles from AP and CP, shows a tight central cluster of AP articles focused on policy diffusion. These works tend to focus on the spread of policies across the American states (with some additional studies of localities and of the centralization or decentralization of policy making); and they cite one another extensively, as represented by their many connections and close proximity. In contrast, the diffusion studies in CP are more scattered. Policy and norm diffusion studies within CP are interspersed with one another and are loosely connected to the AP literature.Footnote 3 The CP literature on the spread of democracy and other governmental forms, shown in the upper left of figure 1, is mostly separate from other CP diffusion literatures as well as AP policy diffusion studies.

Figure 2, which focuses on studies in AP and IR, again shows a tight cluster of AP articles about policy diffusion. The IR literature also features fairly tight clusters of articles, studying the diffusion of conflict, norms, and policy. There is some overlap between studies of norms and policy diffusion within IR, but very limited overlap with AP in these areas, even though both subfields extensively examine policy diffusion. Notably, policy diffusion research in IR is more closely connected to AP diffusion studies through the IR work on norms than directly. This illustrates a substantial disconnect, indeed.

Figure 3, which depicts the literatures in CP and IR, presents the greatest overlap across subfields. In particular, the CP and IR literatures on the diffusion of policy and norms are completely interspersed, and we were unable to isolate distinct clusters between CP and IR on these topics. As in figure 2, however, once again there is a separate cluster of IR studies that investigate conflict. More generally, relative to the AP clusters shown in figures 1 and 2, the nodes here are spread farther apart, indicating fewer citation connections across articles on average.

Notably, policy diffusion research in IR is more closely connected to AP diffusion studies through the IR work on norms than directly. This illustrates a substantial disconnect, indeed.

Together, these three figures show patterns both across and within subfields. We find that certain topics—for example, democratization in CP or conflict in IR—tend to be studied in isolation from topics in other subfields (and even, to a lesser extent, from studies within the same subfield). At the same time, for the topics of policy diffusion and norm diffusion, overlaps exist, indicating that there are substantial opportunities for conversations across subfield lines. Now, we turn our focus specifically to these two types of diffusion.

In tables 1a and 1b we restrict our analysis to the 311 articles that focused on norm and/or policy diffusion. Table 1a provides a breakdown of these articles by topic. Studies of policy diffusion are more common and appear with similar frequency across all three subfields. Studies of the diffusion of norms occur less frequently and appear in only the IR and CP subfields, with roughly equal frequency in these two subfields.

Table 1a Type of Diffusion Articles by Subfield

Table 1b shows the number of citations by each article type (e.g., AP policy) to each of the other article types (e.g., CP policy), again restricted to the set of 311 articles on policy or norm diffusion. The first row illustrates the strikingly insular nature of the AP policy work. Together, AP studies of policy diffusion cited 295 other articles about policy or norm diffusion, and fully 96% of these citations—283 out of 295—were to other AP policy articles, while only 2% of citations were to IR articles and another 2% were to CP articles. The second row, which focuses on citations by CP policy diffusion articles, shows a greater openness by CP policy scholars who cite IR policy and (especially) AP policy diffusion studies in combination nearly as frequently as their own works. The fourth row shows that IR policy diffusion scholars are equally open; in fact, there are more citations to AP and CP policy diffusion articles combined than to other IR policy diffusion articles. In addition, IR policy articles cite IR norms diffusion articles nearly as frequently as they cite other IR policy diffusion articles.Footnote 4

Table 1b Citations by Type of Article and Subfield

Note: Cells indicate the number of citations to this group of articles (columns) by this group (rows). Values in parentheses indicate the number expected if each type of article were equally likely to cite each type of article (parenthetical row and column totals may not add up due to rounding). The null hypothesis of such equal citations is easily rejected (χ2=1914.7, p < 0.001).

Table 1b also demonstrates that AP policy diffusion articles are the most cited of all policy diffusion articles (as shown in the first column). Despite comprising only 27% of all policy or norm diffusion articles (i.e., 83 of the 311 articles included in table 1a), they receive the majority of citations by all policy and norms diffusion articles across subfields. Specifically, table 1b shows that 66% (349 of 526) of the citations found in these articles are to AP studies of policy diffusion.Footnote 5 CP and IR policy diffusion articles, in contrast, receive only 19% (101 of 526) and 8% (40 of 526) of the citations, respectively. The overall bias toward citations within subfield and within topic is evident down the main diagonal in figure 1b, accounting for 73% of all citations.Footnote 6

These general tendencies conform to previous characterizations of the subfields. For example, CP has been described as lacking well-defined research programs (Mahoney Reference Mahoney2007, 116) and as a “fragmented discipline, one that continually struggles to reconcile broad, transportable general theory with context-specific, contextually relevant knowledge” (Caporaso Reference Caporaso2000, 699–700). But one benefit to the subfield is its diversity and ecumenical approach, often drawing on literature in other subfields to build and test arguments. In contrast, AP diffusion scholarship is built on a common base yet is open to Pierson’s (2007, 146) charge that “Americanists have much to sell, but it would be far healthier if they were also very active buyers in the intellectual market place.”

COMMON CULPRITS: TERMINOLOGY, METHODOLOGY, SUBFIELD JOURNALS?

The analysis thus far has demonstrated that substantial connections exist between the IR and CP diffusion literatures dealing with norms and policies, with IR articles especially likely to cite CP articles. In addition, CP articles frequently cite AP policy diffusion articles. Connections between AP and IR policy diffusion studies, however, are almost nonexistent. What might explain these patterns? Is the methodological reliance on quantitative research in AP a barrier to exchange with subfields that feature more qualitative research? Do differences in terminology across these literatures keep them isolated from one another? Does the degree of overlap across journals play a role? In this section, we explore each of these potential culprits that might be responsible for the lack of cross-subfield work, opening a window into divisions in political science that may extend well beyond the diffusion literature.

Regarding terminology, as noted previously, we identified articles by various search terms, and here we focus on the five that are most prevalent in the study of the spread of norms and policies: diffusion, convergence, race to the bottom, policy transfer, and harmonization. Table 2a shows the number of each type of article that predominantly uses each of these five terms. As this table shows, terminology certainly has the potential to be divisive across subfields, as some terms are used nearly exclusively in a single subfield (“race to the bottom” in AP, “policy transfer” in CP, and “harmonization” in IR). Moreover, “convergence” is the most commonly used term in CP and IR, but is dramatically outpaced by “diffusion” in AP.

Table 2a Articles by Terminology Used

Note: Cell entries indicate the number of articles of this type (rows) that mainly used this terminology (columns). The totals add to 310, rather than 311 from Table 1a because one CP: Norms article primarily used the term “contagion.”

Table 2b reveals the extent to which chosen terminology matters in connecting scholars and their articles. The results are dramatic. Relative to the values in parentheses, reflecting expectations of equal citations across terms, we find a strong bias toward the citation of articles that use the same terminology. For example, as shown in the first row of table 2b, 92%—that is, 355 of 387—of citations by articles using “diffusion” terminology are to other articles that do the same. This is remarkably high, especially because diffusion-terminology articles only comprise 42% of all articles (129 of 310) shown in table 2a. Therefore, diffusion-terminology articles draw on insights from articles using other terminology (i.e., convergence, race to the bottom, policy transfer, and harmonization) only 8% of the time. Articles using other terminologies exhibited similar (although less severe) biases.Footnote 7 In sum, 78% of citations across all of these categories were within-terminology, compared to an expected 31% that would have occurred if there were there no within-terminology bias.Footnote 8 Such a bias, combined with the different terminologies used across subfields, matches our finding of a closer alignment between CP and IR than with AP. It also explains further anomaly—for instance, the AP articles on the lower right of figure 2, outside of the main cluster, are almost entirely studies of a potential “race to the bottom,” mainly in US state welfare policy, separated from the others seemingly only by a terminological choice to not describe their phenomena in terms of “diffusion.”

Is the methodological reliance on quantitative research in AP a barrier to exchange with subfields that feature more qualitative research? Do differences in terminology across these literatures keep them isolated from one another? Does the degree of overlap across journals play a role?

Table 2b Citations by Terminology Used Being Cited

Note: Cells indicate the number of citations to this group of articles (columns) by this group (rows). Values in parentheses indicate the number expected if each type of article were equally likely to cite each type of article (parenthetical row and column totals may not add up due to rounding). The null hypothesis of such equal citations is easily rejected (χ2=1200.2, p < 0.001).

With regard to methodology, we code articles as using mainly a quantitative or a qualitative approach.Footnote 9 As shown in Table 3a, 72% of AP policy diffusion articles are quantitative in nature, whereas 65% of CP and 80% of IR diffusion articles are qualitative. Perhaps these differences explain the scholarly divide, most pronounced between AP and IR. To explore the extent of any within-method bias, Table 3b shows the number of citations across these articles both by and to each type of method. As shown in the first row, quantitative articles exhibit an in-method bias, citing other quantitative articles 91% of the time, despite comprising only 40% of the articles being examined. Interestingly, however, qualitative work, too, exhibits a quantitative bias, citing within-method only 46% of the time. When we look at these results in conjunction with subfield affiliation, we find that 60% of all cites are “within category” such as quantitative AP articles citing one another or qualitative IR norms articles citing one another. This is four times the rate expected if articles exhibited no biases in citing other articles, and instead cited other articles randomly, without regard to subfield or method.Footnote 10

Table 3a Articles by Methodology Used

Note: Cell entries indicate the number of articles of this type (rows) that used this methodology (columns). The totals add to 267, rather than 311 from Table 1a because some articles use both methodologies (mixed methods) or neither (such as formal modeling work).

Table 3b Citations by Methodology Used

Note: Cells indicate the number of citations to this group of articles (columns) by this group (rows). Values in parentheses indicate the number expected if each type of article were equally likely to cite each type of article (parenthetical row and column totals may not add up due to rounding). The null hypothesis of such equal citations is easily rejected (χ2=628.5, p < 0.001).

Finally, we considered the effect of journal outlet in producing or entrenching subfield divides. To do so we coded each of the 53 journals in two different ways. First, we coded each journal strictly as AP, CP, or IR based solely on which subfield published the most norm or policy diffusion articles in that journal. Second, we recoded many of these journals as “multi-subfield” if they are widely regarded as “general journals” or if they are known for publishing mainly in one subfield but instead publish diffusion articles mainly from a different subfield.Footnote 11 Tables 4a and 4b show our analysis based on the first coding scheme. As seen in Table 4a, and as expected, most articles are published in journals dominated by their own subfield, such as 96% of AP policy diffusion articles published in predominantly AP journals. That said, 18% of CP articles are published in AP journals and 15% in IR journals, while 18% of IR policy diffusion articles are published in predominantly CP journals.

Table 4a Articles by Main Subfield of Journal

Note: Cell entries indicate the number of articles of this type (rows) published in journals most frequently publishing this subfield’s diffusion work (columns).

As shown in Table 4b, citations largely stayed within subfields, based on journal placement, with other AP journal articles accounting for 81% of citations in AP journal articles, and with within-subfield journal citations accounting for 76% of all citations overall.Footnote 12 The largest crossovers in the first row are AP journal articles citing those in CP. The middle row shows about equal crossover from CP to AP as to IR, whereas the bottom row shows robust crossover to CP from IR, but not to AP.

Table 4b Citations by Journal’s Subfield

Note: Cells indicate the number of citations to this group of articles (columns) by this group (rows). Values in parentheses indicate the number expected if each type of article were equally likely to cite each type of article. The null hypothesis of such equal citations is easily rejected (χ2=1243.7, p < 0.001).

The second analysis, using “multi-subfield” journals is somewhat less informative along these lines, because a majority of AP and CP policy diffusion articles are published in these journals, and because “multi-subfield” journals receive 81% of the citations.Footnote 13 This indicates, unsurprisingly, that publications in top general journals are more likely to be noticed and attract citations. More relevant for our present purposes, and consistent with the first coding scheme, we find that of those articles published in subfield (rather than “multi-subfield”) journals, 85% of citations are within-subfield journal, with zero crossovers from other subfields to AP journal articles or vice versa. IR and CP scholars pay little attention to articles that, although they might be relevant, are published in AP journals; and AP scholars unfortunately return the favor.

IMPLICATIONS AND CONCLUSIONS

We set out to explore whether and why subfield boundaries separated scholars who ostensibly should be drawing on one another’s ideas, approaches, and findings. To do so, we chose an area of inquiry—diffusion studies—in which similar topics stretch across subfields. We found, as should be expected, that when the topics of interest varied from one another, such as conflict versus democratization, there was little overlap in citation networks. Put simply, scholars working on these different topics were not highly connected to one another. However, even when scholars were studying similar phenomena, such as the spread of policies or of norms across governments, the network ties were inconsistent.

A recent turn in policy diffusion scholarship emphasizes the importance of specifying the mechanisms of diffusion—that is, not simply when, but how diffusion occurs (e.g., Berry and Baybeck Reference Berry and Baybeck2005, Braun and Gilardi Reference Braun and Gilardi2006, Shipan and Volden Reference Shipan and Volden2008). Learning is one prominent mechanism of diffusion whereby policy makers take note of effective policies tried elsewhere and subsequently choose to adopt those policies, thus facilitating diffusion. Our analysis suggests that subfield boundaries, reinforced by differences in terminology, methodology, and journal placement, prevent scholars from learning from one another.

Scholars couching their research in terms of “policy transfer,” a “race to the bottom,” or “harmonization” may be speaking with one another; but they are neither engaging nor subsequently being engaged by the larger group of scholars who describe their work mainly with the term “diffusion.” Elsewhere we offer a humorous list of 104 separate terms that scholars have developed to describe different diffusion processes (Graham, Shipan, and Volden Reference Graham, Shipan and Volden2013). Here our research suggests that scholars may instead benefit from seeking to come to a common understanding of a much smaller number of groupings, and to discuss them with a common terminology.

On methodological grounds, we found a large divide between the quantitative studies in AP and the qualitative studies in CP and IR. Coupled with a bias toward citing studies that use similar methodology (especially among quantitatively-oriented scholars), it seems clear that methodological divides are keeping scholars from building on one another’s findings. In our view, more could be done to bridge these methodological divides. For instance, the Empirical Implications of Theoretical Models initiative has sought to break down methodological divides between empirical and theoretical approaches. In so doing, more articles presenting formal theory results tend to discuss their testable implications. In a similar way, qualitative scholars (and referees of their works) could take further steps to concretely lay out whether general, systematic, and testable expectations follow from their qualitative findings. And quantitative scholars could highlight areas for future qualitative work, or could offer more accessible and thorough qualitative analyses of their own to motivate their hypotheses or to fill in where available data may fall short.

While subfield-specific journals may serve a variety of useful purposes, they also clearly reinforce disciplinary boundaries. This suggests a useful role to be played by referees and editors from other subfields, even in subfield-specific journals. The practice may be particularly appropriate for topics like diffusion studies, which lend themselves to cross-subfield scholarship. The selection of outside referees has the potential to enlighten both author and reviewer by broadening the scope of feedback for the author and providing referees with insights into alternative ideas and perspectives on familiar political processes.

While subfield-specific journals may serve a variety of useful purposes, they also clearly reinforce disciplinary boundaries. This suggests a useful role to be played by referees and editors from other subfields, even in subfield-specific journals.

In sum, we find diffusion scholarship to be prone to these barriers despite its status as a most likely case for integration. Unlike some other topics, this is an area where all three subfields are focused on similar questions related to the political conditions that facilitate the spread of policies and norms across governments. If integration is far from complete in this case, we would not expect high levels elsewhere, where various connections could prove useful even if they are less obvious.

There are clear costs associated with these divisions. Without an awareness of developments in other subfields, scholars miss opportunities to spur new ideas, counter old arguments, and build on one another’s accomplishments. And as a discipline, we may be more subject to reinventing the wheel, with what appear to be new and novel contributions containing ideas found elsewhere—only using different terminology or appearing in journals in a different subfield.

We conclude with one glimmer of hope, however, based on the increasing use of coauthorship in political science today. Scholars who “sit at multiple tables” or those who work with coauthors who straddle these divisions can overcome some of the problems described previously. For instance, Harvey Starr’s (1991; along with Siverson and Starr Reference Siverson and Starr1990) work on contagion effects associated with conflict and with democracy provides an example of the former, as the arguments draw on and tie into literatures prevalent in both IR and CP. And Michael Mintrom’s studies with coauthors in AP (e.g., Mintrom and Vergari Reference Mintrom and Vergari1998) and IR (e.g., True and Mintrom Reference True and Mintrom2001) provide an example of the latter. Indeed, greater integration between IR and CP in diffusion studies and between international and comparative political economy more generally has been facilitated by such scholars (e.g., Milner Reference Milner1998; Simmons, Dobbin, and Garrett Reference Simmons, Dobbin and Garrett2006), in an encouraging trend that we hope to see continue.

ACKNOWLEDGMENTS

We thank Rachel Schneider and Mike Zilis for valuable research assistance and Derek Stafford for helpful assistance with network analyses. Throughout our broad assessments of connections across disciplines in the study of diffusion, we have gained immensely from the generous comments of the editors and anonymous reviewers at PS, as well as Jenna Bednar, Bill Berry, Fred Boehmke, Graeme Boushey, Sarah Brooks, Claire Dunlop, Lorraine Eden, Rob Franzese, Katharina Füglister, Fabrizio Gilardi, Virginia Gray, Don Haider-Markel, Andrew Karch, David Levi-Faur, Covadonga Meseguer, William Minozzi, Chris Mooney, Ben Noble, Julie Pacheco, Aseem Prakash, Claudio Radaelli, Harvey Starr, Diane Stone, Hugh Ward, Kurt Weyland, and Alan Wiseman. All errors in description and interpretation remain our own.

Erin R. Grahamis an assistant professor in the department of history and politics, at Drexel University. She can be reached at .

Charles R. Shipanis the J. Ira and Nicki Harris Professor in the department of political science at the University of Michigan. He can be reached at .

Craig Voldenis a professor of public policy and politics in the Frank Batten School of Leadership and Public Policy at the University of Virginia. He can be reached at .

APPENDIX

APPENDIX Table A1a Articles by Journal (including Multi-Subfield Journals)

Table A1b Citations by Journal’s Subfield (including Multi-Subfield Journals)

Footnotes

1. Specifically, we searched the top 50 journals according to Giles and Garand’s (Reference Giles and Garand2007) list, as well as American Politics Research, Governance, and Publius, as these latter three have published substantial numbers of diffusion studies. We used the search terms “diffusion,” “contagion,” “convergence,” “harmonization,” “policy transfer,” and “race to the bottom,” identifying all articles using these terms in the title or abstract. We then read the abstracts of the identified articles, eliminating those that were not about diffusion, broadly defined.

2. The figures are generated with the commonly-used Fructerman-Reingold energizing algorithm, which accounts not merely for citations but also for the extent to which an article’s citations in turn cite common articles, and so on until an equilibrium is reached. Details are offered in Graham, Shipan, and Volden (Reference Graham, Shipan and Volden2013).

3. Not all articles in the encircled clusters are of the type labeled, nor are all of that type of article within the demarcated cluster. Nevertheless, a close examination of the nodes clearly shows the types of groupings labeled in the figures.

4. Among key policy diffusion articles, Ross and Homer (Reference Ross and Homer1976) is the most likely to be cited in both CP and IR articles. However, two central CP articles (Bennett Reference Bennett1991; and Collier and Messick Reference Collier and Messick1975) remain more likely to be cited by fellow CP scholars than by their IR counterparts. In contrast, Haas’s (Reference Haas1992) IR norm diffusion article is cited frequently in both CP and IR articles.

5. These findings, combined with those in the previous paragraph, explain the tight clusters found for AP articles in figures 1 and 2.

6. One way to assess such a bias systematically is to look at the numbers in parentheses in table 1b, which indicate the expected number of citations if each type of article were equally likely to cite each other type of article, accounting for the number of articles in each category. As noted down the main diagonal, scholars disproportionally cite within their own subfields and topics of study. A chi-squared analysis shows this own-category citation bias to be highly significant (p < 0.001).

7. Harmonization articles constitute 13% of the total articles – 41 of the 310 articles included in table 2a – but table 2b shows that they cite within-terminology 69% (11 of 16) of the time. “Race to the bottom” and “policy transfer” cite within-terminology 46% (12 of 26) and 26% (15 of 57), respectively, despite constituting only 6% and 5% of the total articles. Convergence scholarship is the most ecumenical of the group, making up 33% of the total articles and citing within-terminology 42% of the time.

8. A chi-squared analysis shows a great degree of within-terminology citation bias (p < 0.001).

9. For our present purposes, we set aside articles using both (or neither) of these methods, thus limiting our sample for Table 3 to 267 articles and 409 citations. Articles were coded as quantitative if they employed statistical analysis on a large sample, conducted large N survey research, or used experimental methods. Articles were coded as qualitative if they employed single case or comparative case studies and relied primarily on interviews, process tracing, ethnography, or other qualitative methods.

10. A chi-squared analysis shows a strong within-method bias regardless of subfield (p < 0.001).

11. Some of the 53 journals published no articles on norm or policy diffusion. Of those that did, we classified them in the following subfields (journals in bold are labeled as “multi-subfield” in our second coding scheme). In AP: American Behavioral Scientist; American Journal of Political Science; American Political Science Review; American Politics Quarterly/American Politics Research; Journal of Law, Economics, and Organization; Journal of Politics; Journal of Theoretical Politics; Political Geography Quarterly; Political Research Quarterly; Public Administration Review; Publius; Social Science Quarterly; Urban Affairs Review; Western Political Quarterly. In CP: British Journal of Political Science; China Quarterly; Comparative Political Studies; Comparative Politics; European Journal of Political Research; Governance; Journal of Democracy; Journal of Peace Research; Law and Society Review; Political Behavior; Political Geography; Political Psychology; Rationality and Society; Urban Studies; World Development; World Politics. In IR: American Journal of International Law; European Journal of International Affairs; International Affairs; International Organization; International Studies Quarterly; Journal of Common Market Studies; Journal of Conflict Resolution; Journal of Law and Economics; Political Science Quarterly; Politics and Society; Studies in Comparative International Development; Theory and Society; Third World Quarterly.

12. A chi-squared analysis shows a strong within-subfield-journal bias (p < 0.001).

13. Details of this analysis are offered in Appendix tables A1a and A1b.

Note: Cell entries indicate the number of articles of this type (rows) published in journals dominated by this subfield’s work (columns).

Note: Cells indicate the number of citations to articles published in this group of journals (columns) by this group of journals (rows). Values in parentheses indicate the number expected if each type of article were equally likely to cite each type of article (parenthetical row and column totals may not add up due to rounding). The null hypothesis of such equal citations is easily rejected (χ2=324.4, p < 0.001).

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Figure 1 Diffusion Studies in American and Comparative Politics

Figure 1

Figure 2 Diffusion Studies in American Politics and International Relations

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Figure 3 Diffusion Studies in Comparative Politics and International Relations

Figure 3

Table 1a Type of Diffusion Articles by Subfield

Figure 4

Table 1b Citations by Type of Article and Subfield

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Table 2a Articles by Terminology Used

Figure 6

Table 2b Citations by Terminology Used Being Cited

Figure 7

Table 3a Articles by Methodology Used

Figure 8

Table 3b Citations by Methodology Used

Figure 9

Table 4a Articles by Main Subfield of Journal

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Table 4b Citations by Journal’s Subfield