A Social Network Analysis of Publishing Networks in the “Big 5” Journals in Criminology and Criminal Justice

Abstract

Prior research has frequently employed various methods for investigating issues surrounding publication productivity and authorship, including examinations of the number of co-authors in peer-reviewed journal articles and the order of authorship. Relying on 5 years of data from publications from the “Big 5” journals in criminology and criminal justice (i.e., Criminology, Journal of Research in Crime & Delinquency, Journal of Quantitative Criminology, Crime & Delinquency, and Justice Quarterly), the current study extends this extant research by providing a social network analysis of publishing networks. Results are consistent with previous findings, suggesting that publishing networks are largely decentralized, although key networks and definitive leaders in these networks exist as well. In addition, several authors were identified that have significant leverage over the publishing networks. Study limitations and directions for future research are also discussed.

Keywords

publication productivity social network analysis rankings criminology criminal justice

Introduction

Publication productivity has been a topic of much conversation in the academic literature in general and in the criminology and criminal justice (CCJ) academic literature more specifically. This genre of science has typically focused on a host of topics including efforts to identify academic “stars” in the discipline, ranking journals in the discipline, ranking departments/programs in the disciplines, and investigating other publication productivity metrics (i.e., number of co-authors, etc.) (Barranco et al., 2016; Cohn & Farrington, 1998; Copes et al., 2012; Fabianic, 2001, 2002; Cohn & Farrington, 2007; Frost et al., 2007; Jennings et al., 2009; Jennings, Gibson, et al., 2008; Jennings, Schreck, et al., 2008; Khey et al., 2011; 2017; Kleck et al., 2007; Kleck & Mims, 2017; Long et al., 2011; Parker & Goldfeder, 1979; Poole & Regoli, 1981; Rice et al.,2005, 2007; Shichor et al., 1981; Shutt & Barnes, 2008; Sorensen et al., 1992, 2006; Sorensen & Pilgrim, 2002; Stack, 1987; Steiner & Schwartz, 2006). Importantly, these publication productivity metrics have also been explored for female scholars, minority scholars, and for programs beyond those that only house doctoral programs (Ahlin, 2020; del Carmen & Bing, 2000; Greene et al., 2018; Potter et al., 2011; Rice et al., 2007). Although this prior research has contributed greatly to providing an empirical resource to the CCJ field, much less is understood regarding whether “invisible colleges” or publishing networks exist in the CCJ discipline and how these networks may (or may not) influence the scholarship that resides in our discipline’s top-tier “Big 5” journals, particularly for scholars in the United States (i.e., Criminology [CRIM], Journal of Research in Crime & Delinquency [JRCD], Journal of Quantitative Criminology [JQC], Crime & Delinquency [C&D], and Justice Quarterly [JQ]). With recognition of this gap in the literature, the current student provides an exploration into these very concepts through the application of a social network analysis framework.

Literature Review

Trends in Publishing

Since publication productivity plays such an important role in scholarly communication, several studies have been conducted to discover patterns, one of which is Lee and Bozeman’s (2005) study of the relationship between collaboration and publishing productivity. Specifically, they consulted CVs and administered surveys to academics affiliated with American universities. In the 3 years after their surveying phase, they monitored the normal count and fractional count for journal publications of each of the participants. The normal count referred to all of a participant’s peer-reviewed journal articles in the 3-year time frame (2001–2003), whereas the fractional count was determined by dividing each of the publications by the number of co-authors. Lee and Bozeman (2005) found that while the normal count of publications did demonstrate a strong correlation with publishing productivity, the fractional count and publishing productivity lacked a significant relationship. Furthermore, they found that senior faculty members tended to collaborate in more of a mentor capacity, possibly creating opportunities for less experienced researchers to work alongside them and benefit from that type of information-sharing. Having said this, one of the limitations of this study was that it only focused on the productivity of collaboration versus other aspects such as the quality of the work that is produced.

More recently, Azoulay et al. (2010) explored the impact that the death of a “star” researcher can have on publishing activity within their field. To conduct their analysis, Azoulay et al. (2010) applied concepts from microeconomics treating deaths of premier life scientists as shocks to the structure of the “intellectual neighborhoods” that they were once a part of and later analyzed the boundaries of those neighborhoods after their deaths. Their findings indicated that after the death of an eminent life scientist, publications from their collaborators tend to decline while non-collaborators’ contributions show an increase. This increase in contributions from individuals who were previously outsiders to that particular subfield can push research into a new direction. Ultimately, while publication activity, in general, increases slightly following the death of a primary life scientist, the results vary when considering collaborator publication and non-collaborator publication (Azoulay et al., 2010).

Invisible College

Although the term “invisible college” has been used since the 1960s to refer to the methods in which scholars share knowledge with each other despite a lack of proximity, Zuccala (2006) advocates for the development of a more clear and coherent definition of the term. In an effort to rectify this, Zuccala (2006) proposes a new “structurally informed value-added model” to guide the future study of invisible colleges. This model is based on the examination of three key aspects of an invisible college: (a) subject specialty, (b) social actors, and (c) information use environment. He argues that just as microbiologists need microscopes to properly examine items relevant to their field of study, so too do researchers who wish to learn more about the inner workings of invisible colleges (Zuccala, 2006). By studying the rules and research problems within a discipline, how the scholars in that discipline coordinate to further their collective knowledge, and the settings in which this information sharing occurs, further research will be able to provide better insights into how invisible colleges function.

Social Network Analysis

In the 1940s and 1950s, the use of social network theory began to expand and flourish, yet the underlying idea of replicating aspects of physical sciences in the social sciences dates back to a century beforehand (Borgatti et al., 2009). Social network theory can be used to analyze a wide variety of social phenomena ranging from behavior at the individual level to much larger organizations such as corporations or governments. By mapping out social networks, researchers can more easily recognize patterns and draw conclusions about the strengths and weaknesses within the system. In this vein, Borgatti et al. (2009) demonstrated the flexibility and utility of social network analysis citing a multitude of uses from measuring communication efficiency between the nodes in a structure to its use in apprehending crime syndicates and terrorist organizations. They also argued that one of the fundamental rules of social network analysis is that a node’s success can be determined by its position within the network. This suggests that mapping out a network’s nodes could provide valuable insights not only into how the network is structured but also how that structure may influence the nodes.

Since its conception, the use of social network analysis has been adapted to fit many purposes and has grown significantly in popularity. For example, in a special issue for Quality and Quantity, Park (2020) highlighted a variety of articles to showcase the modernization of social network analysis techniques in Asia and their contributions to World Association for Triple Helix and Future Strategy studies. Furthermore, Park (2020) discussed several novel applications of social network analysis such as studying national economies, examining German policy-making processes, and utilizing hashtags to identify influential figures on Twitter. The heterogeneity of the studies that Park (2020) analyzed also demonstrated the versatility of the social network analysis technique and how it can be applied to a variety of fields, including criminology.

More specifically, Bichler and Malm (2008) utilized social network analysis to examine Environmental Criminology and Crime Analysis (ECCA) symposiums as an indication of how community has changed over time. After mapping the individuals who typically attend ECCA symposiums, they discovered three individuals were responsible for recruiting more than 75% of attendees indicating that the organization is at risk for serious fragmentation because of how ties within the organization are distributed. For instance, their analysis revealed an uneven geographical distribution of community members, which indicated to Bichler and Malm (2008) that future efforts may need to be directed toward hosting meetings in other locations to improve the representation of other regions within the group.

Publishing Networks

A variety of publication productivity studies in CCJ, in general, have been conducted to discern how scholars interact to disseminate knowledge by looking at things such as collaboration, publishing productivity, and citation counts (Cohn et al., 2000; Copes et al., 2012; Khey et al., 2011; Orrick & Weir, 2011; Roche et al., 2019). Similarly, a handful of recent studies have observed the structures of different publishing networks in CCJ from a social network analysis framework. For example, González-Alcaide et al. (2013) analyzed publications from the Criminology & Penology section of the Journal Citation Reports. The publications analyzed spanned 19 different journals and focused on the articles published between 2005 and 2009. Their findings were consistent with existing research on the topic showing a slight increase in publications over time with 133 more articles published in 2009 than in 2005. The authors also found a continued increase in multi-authorship, noting that the average number of authors per work was 2.24 and that the majority of the 4,696 papers were authored by at least two individuals. González-Alcaide et al. (2013) also pointed out the fairly nationalized focus of this particular publication network as only 9.74% of the articles’ authors indicated international collaboration.

Most recently, Fenimore et al. (2021) conducted a similar study but instead focused on the publication network evidenced in the Journal of Experimental Criminology (JEC) articles published between 2011 and 2020. Specifically, the authors examined 298 articles published by 678 authors to learn more about the “invisible college” responsible for transmitting knowledge within the field of experimental criminology. Their results indicated that the network is fairly decentralized, however, there were a few identifiable key authors. Similar to Bichler and Malm (2008), the authors concluded that the removal of these authors who play pivotal roles could cause fragmentation within the network potentially slowing the dissemination of knowledge throughout the field. In addition, Fenimore et al. (2021) suggested further research into publication networks in other journals or disciplines as well as the consideration of historical trends to examine how those social networks have evolved over time would be a useful endeavor.

The Current Study

The existing literature on publication productivity in CCJ has largely focused on individual academics, identifying who is the most prolific (including who is most prolific under a variety of demographic groupings), as well as which journals are the most prolific and most cited (Cohn et al., 2000; Copes et al., 2012; Khey et al., 2011; Orrick & Weir, 2011; Roche et al., 2019). However, there has been very little research examining the publishing networks for either prolific faculty or within or between top-tiered CCJ journals, particularly those that are typically highly ranked in the United States. As such, the present study is informed by the preliminary findings of Fenimore et al. (2021) and extends the idea of publishing networks to a broader set of the “Big 5” CCJ journals. Specifically, the current study focuses on identifying networks of scholars in the field, the journals that the authors (and networks) publish in, along with exploring the measures of centrality and identifying key authors within these networks.

Method

Data

The data used for this analysis are derived from a manually constructed database of all articles published in the five most consistently prominent journals in the CCJ field from 1985¹ to 2019. These journals were specifically selected because they had been listed as the top five peer-reviewed outlets at the time of the original data collection efforts based on impact factors and prior publication productivity/ranking studies from the Journal of Criminal Justice Education. Using the website for each journal, we gathered key information about each article published, including the number and names of the author(s), the gender of the lead author, the title of the article, and the page length. We excluded papers that were identified as corrections (or corrigendum), announcements, editor’s notes, and other miscellaneous publications (e.g., presidential addresses). Our final database includes 4,940 articles published between 1985 and 2019.

For the purposes of this study, we only included publications from between the years 2015 and 2019. According to Roche et al. (2019), the total number of collaborating authors significantly increased in the mid-2010s. Given this increase, as well as the increased opportunities for informal learning and networking, this pre-pandemic time period is likely to represent publishing networks at the height of collaborative publication practices. We also chose to stop data collection in 2019 because publishing patterns and behaviors changed with the onset of the COVID-19 pandemic and may require a separate analysis on their own. The final analysis sample includes 1,021 publications and represents the work of 1,482 individual authors.

Each author in this dataset has published an average of 1.80 (SD = 1.85) articles. For those authors with two or more publications (N = 463), the average number of publications increases to 3.57 (SD = 2.54). Thirteen authors have either authored or co-authored 10 or more publications, with the five most published authors having an average of 17.80 publications (SD = 7.40). Two of these five (Anthony Braga and John Hipp) have published in each of the Big 5 CCJ journals. The remaining three authors (Alex Piquero, Daniel Mears, and Justin Pickett) have published in four of the five top CCJ journals (Table 1) during this time frame.

Table 1.

Descriptive Statistics for Individual Authors.

Journal Name	Articles	Individual authors	Average authors/Publication (SD)	Min/maxAuthors	Average publications/Author (SD)	Min/maxPublications
	1,021	1,482	2.62 (1.27)	1-10	1.80 (1.84)	1-31
C&D	347	679	2.61 (1.31)	1-8	1.33 (0.95)	1-16
CRIM	131	264	2.51 (1.23)	1-7	1.25 (0.63)	1-5
JQC	167	370	2.69 (1.28)	1-10	1.22 (0.67)	1-7
JRCD	146	324	2.72 (1.32)	1-7	1.24 (0.60)	1-5
JQ	230	446	2.57 (1.22)	1-7	1.32 (0.71)	1-5

Note. C&D, Crime & Delinquency; CRIM, Criminology; JQ, Justice Quarterly; JQC, Journal of Quantitative Criminology; JRCD, Journal of Research in Crime & Delinquency.

Analysis

In social network analysis, there are four key measures that need to be examined to understand the network centralization and identifying the key leaders: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Each of these measures allows us to understand who plays central roles in the CCJ literature, within and between the top five publishing outlets in the field. We recommend three key resources for other authors interested in pursuing research on similar topics, or using this methodology. Specifically, Lu et al. (2010) provide detailed descriptions of the four primary measures of centrality. Similarly, Scott (2011) and Wasserman and Faust (1994) provide useful resources for a better understanding of the history and theory underlying social network analysis, and they describe the technique in layman’s terms to assist anyone interested in learning the necessary foundational knowledge about social network analysis.

Results

Full Network and Component Analysis

Each node in the full publishing network (Figure 1) represents a unique individual author in the dataset (N = 1,482 total nodes). The links between the nodes represent a shared publication between two authors (N = 4,006 links; 2,524 non-self-loops and 1,482 self-loops). Although central figures can be identified (see Table 1), the network itself is not overly centralized or dense, having a total density of 0.004. Such decentralization can have significant implications for the fragmentation of the network (Azoulay et al., 2019; Fenimore et al., 2021).

Decentralization is better displayed when examining the full publishing network’s constituent components. Each component constitutes its own sub-network within the overall network. As such, isolates—authors who have solo-authored publications in the Big 5, but who have not co-authored publications in the Big 5—are disconnected from the overall network displayed in Figure 1. If we were to “hide” the 80 isolates in the network in Figure 1, they would be removed from the image.

Components (see Table 2), or the separate sub-groups of authors, can include one or more authors that have shared publications. However, they are not necessarily connected to another sub-group. That said, despite being decentralized, there is one significantly larger component comprising 826 authors across the Big 5 journals (“the central network”) that anchors the overall network. There are 266 total components in this network, which range from one author (isolates) to 826 authors (the central network). This supports a similar conclusion derived from Fenimore et al.’s (2021) analysis in which there are smaller, more specified networks that exist within the larger network.

Table 2.

Component (Publishing Subgroups) Descriptive Statistics.

Component size	N	%	Links	Density
Isolate	80 (80 authors)	30.08 (5.40)	—	—
Dyad	82 (164 authors)	30.83 (11.07)	—	—
Triad	54 (162 authors)	20.30 (10.93)	—	—
4-person	24 (96 authors)	9.02 (6.48)	7–10	0.70–1.00
5-person	11 (55 authors)	4.14 (3.71)	10–15	0.67–1.00
6-person	7 (42 authors)	2.63 (2.83)	15–21	0.71–1.00
7-person	2 (14 authors)	0.75 (0.94)	17–28	0.61–1.00
8-person	2 (16 authors)	0.75 (1.08)	30–36	0.83–1.00
9-person	3 (27 authors)	1.13 (1.82)	24–32	0.53–0.71
826-person	1 (82 authors)	0.38 (55.74)	2,682	0.008
Total	266 (1,482)	100.00 (100.00)

This network includes 80 isolates, 82 dyads, 54 triads, and 50 larger networks with an average of approximately 22 authors (SD = 114.93) per publishing sub-group. Isolates, dyads, and triads are solo-, two-, and three-author subgroups that are not directly connected to the primary network. The largest network includes >55% of all the authors in the dataset (N = 826), despite >50% of the network being comprised of the disconnected isolate, dyad, and triad components. The number of links describes the number that one node has within that component. For example, in the 5-person component size, the links range from 10 to 15. This means that one node (author) is connected to four other authors in one of the publications with five authors. The density of 5-person components ranges from 0.67 to 1.00, which means that in at least one of these components, all five authors are equally connected by the same number of publications.² As such, smaller components have much higher densities than the central and overall networks.

Publishing Leaders

Figure 2 displays the top 10 overall ranked authors in the overall publishing network.³ These scores were determined by taking the average measure of standardized scores for all 19 centrality measures offered, and calculated by the ORA Social Network Software.⁴ Scores were normalized to values between 0 and 1.00, then authors were ranked by these scores. Figure 2 lists these authors from lowest to highest.

Following recommendations from Lu et al. (2010), four measures of centrality were used to identify publishing leaders among the Big 5 CCJ journals (see Table 3). These four measures, total degree centrality, betweenness, closeness, and eigenvector centrality, were used to identify the authors with the most central positions in the network. In general, these authors make some of the largest contributions to the overall publishing network and are best suited for the transfer of information in the network. Isolating any of these authors from the network could have severe implications for the overall network (Lu et al., 2010).

Table 3.

Authors Holding Key Positions in the Big 5 CCJ Publishing Network.

Rank	Degree centrality		Betweenness		Closeness		Eigenvector centrality
Rank	Author	Value	Author	Value	Author	Value	Author	Value
1	Alex Piquero	0.008	Alex Piquero	0.188	Alex Piquero	2.904^-04	Alex Piquero	0.410
2	Anthony Braga	0.004	David Weisburd	0.059	Christopher Sullivan	2.904^-04	Lorraine Mazerolle	0.222
3	Daniel Mears	0.004	Anthony Braga	0.044	David Weisburd	2.904^-04	Sarah Bennett	0.219
4	Wim Bernasco	0.003	Mark Berg	0.044	Jonathan Intravia	2.904^-04	Kevin Beaver	0.211
5	J.C. Barnes	0.003	Gregory Zimmerman	0.039	Michael Roque	2.904^-04	J.C. Barnes	0.201
6	Anja Dirkzwager	0.003	Justin Pickett	0.039	Holly Nguyen	2.904^-04	Brian Boutwell	0.186
7	Paul Nieuwbeerta	0.003	Elizabeth Groff	0.037	Frank Cullen	2.904^-04	Emma Antrobus	0.177
8	Justin Pickett	0.003	Holly Nguyen	0.034	Thomas Baker	2.904^-04	Elizabeth Eggins	0.177
9	Mark Berg	0.003	Wim Bernasco	0.034	Christi Metcalfe	2.904^-04	Joseph Schwartz	0.176
10	Eric Stewart	0.003	Wouter Steenbeek	0.033	Ray Paternoster	2.904^-04	Michael Baglivio	0.175

The first centrality measure, total degree centrality, indicates the authors that are most likely to diffuse new information, and would be akin to the literature looking at publication counts to identify publishing “stars.” Logically, it follows that these authors have the most connections with other authors. The top five authors in the overall network with the highest degree centrality are Alex Piquero, Anthony Braga, Daniel Mears, Wim Bernasco, and J.C. Barnes.

Authors that are listed as the top 10 with the highest betweenness centrality are known as information gatekeepers. If these authors were isolated from the network, it would interrupt the flow of information across the overall network. Carley (2022, np) provides descriptions of each measure in the analytical output and describes this measure as the centrality of an author’s node in that, “across all node pairs that have the shortest path containing [a specific author], [this is] the percentage that pass through [that author].” Regarding publishing, these authors hold the most power in interrupting normal publishing practices, as they “are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups” (Carley, 2022, np). If these individuals were to stop publishing, their network could potentially be removed from the overall network. Alex Piquero, David Weisburd, Anthony Braga, Mark Berg, and Gregory Zimmerman are the top five authors with the greatest ability to disrupt the network.

More relevant to networking opportunities for students, the next measure of centrality, closeness, measures the average number of connections between one author and another author. This measure is best exemplified through the game, Six Degrees of Separation, in which we can trace our connections to others through who we know. Alex Piquero, Christopher Sullivan, David Weisburd, Jonathan Intravia, and Michael Roque hold the top five positions for closeness.

Finally, the last measure of centrality, eigenvector centrality, measures authors that are well-connected to other well-connected authors. This measure can be useful in identifying leaders among the publishing “groups,” as these leaders are often strongly connected to their publishing group, as well as others in other publishing groups. Alex Piquero, Lorraine Mazerolle, Sarah Bennett, Kevin Beaver, and J.C. Barnes hold the top five positions for eigenvector centrality.

Pivotal Authors

Table 4 displays the names of the authors that are part of two “critical sets.” These authors have the “best reach [to] all other nodes, [and] whose removal maximally disrupts a network” (Altman et al., 2020, p. 1489). These authors therefore inherently exert a strong influence over the network. The first group—Alex Piquero, Gregory Zimmerman, Mark Berg, Anthony Braga, and Matt Vogel—could cause the central network to fragment into several smaller components. Because the overall network is decentralized, this likely would not have a significant impact on the field, nor on the Big 5 CCJ journals, as several of these individuals have published in more than one of these journals. Issues arise when there is a significant degree of overlap between these two critical sets. As it stands, only Alex Piquero is in both groups. However, these authors hold the unique position of being responsible for bringing many other authors to the Big 5 CCJ journals.

Table 4.

Authors Holding the Strongest Influence Over the Big 5 CCJ Publishing Network.

Fragmentation	Information transfer
Alex Piquero	Michael Roque
Gregory Zimmerman	Alex Piquero
Mark Berg	David Weisburd
Anthony Braga	Justin Pickett
Matt Vogel	Christopher Sullivan
Original network fragmentation value = 0.57	Distance weighted reach of set = 0.29
Critical set removal fragmentation value = 0.66

Discussion

The current study was informed by an examination of a publishing network within a specific subfield of CCJ (Fenimore et al., 2021) and extends this application to the wider field to identify key authors in CCJ by examining publication data between 2015 and 2019. More specifically, while Fenimore et al. (2021) examined key authors and paths of informal knowledge transfer in experimental criminology, the current study explored these dimensions on a wider scale by looking at key authors and informal knowledge transfer for the Big 5 CCJ journals (CRIM, JQ, JQC, JRCD, and C&D). Key findings emerged from this effort and are summarized below.

The Big 5 CCJ publishing network is highly decentralized, much like the smaller JEC network (Fenimore et al., 2021). Though this may not be obvious, it is likely intuitive. For example, authors having subspecialties is key to advancing a field that is as wide-reaching and ubiquitous as CCJ, which plays a key role in policy planning and implementation, political campaigning, and is often considered in societal problems like mental health, addiction, and homelessness. Furthermore, academics all can easily name several classical criminology theorists if we were asked, in the same way that we might be able to name several of the seminal publications and their authors in our own subfields.

Given the interest in publishing stars and publishing practices and the growth of such literature, the key individuals identified in the current study should likely come as no surprise to those in the CCJ field who are aware of their contributions. However, what may be surprising is that there are nine authors that hold key positions in preventing the fragmentation of the overall network and have the greatest reach within all of the Big 5 CCJ journals. These authors, Alex Piquero, Gregory Zimmerman, Mark Berg, Anthony Braga, Matt Vogel, Michael Roque, David Weisburd, Justin Pickett, and Christopher Sullivan, are uniquely situated to have a huge potential impact on publishing practices.

More importantly, there is something to be said about publishing practices in general when examined by the apparent genders of the key publishing authors. Of the nine authors with the most influence over the network, none of these authors is female. Similarly, in the list of top-ranked authors based on their centrality measures (Figure 2), only two of these authors are female, and only one is of Hispanic descent. In an age of growing attempts to ensure diversity, equity, and inclusion, more research is needed to highlight the achievements of minority publishing groups including those in marginalized social groups, such as minority racial/ethnic groups and the LGBTQ+ academic community. Faculty also need to be identifying and recruiting students that belong to these marginalized groups and encouraging (and mentoring) them to engage in CCJ research. It is also important to note that the present study only examines journals that are typically highly ranked among scholars primarily in the United States; therefore, there are likely outlets that scholars from other geographical locations may perceive as top outlets that were not included in this study (e.g., the British Journal of Criminology for European scholars).

There are a few limitations that need to be addressed in this research. First, the data only examines five complete years of data. This limits our ability to measure the full extent of the publishing network over the history of publishing practices. However, Roche et al. (2019) note that the average number of authors per publication has increased, implying an increasing trend in group publishing. Because this average was at its highest at the time of this analysis, it supported the use of 5 years of data to capture the effect that this has had on the overall publishing network. However, this does little to understand how the network has changed over time from when fewer authors were publishing fewer articles per year in these five journals. Furthermore, because there is potential for the network to be leveraged by a small number of individuals (see Table 4), future research should also look at the network impact that authors holding central positions have on the network over time, including when publishing “stars” pass (Azoulay et al., 2010).

Second, this article only examines the most widely read journals at the time of data collection (originally 2017, updated in 2022), not those within academic subdisciplines of the field, in which other authors may hold key positions, nor does it account for journals that are more prominent outside of the United States. In addition, the “Big 5” journals in CCJ have changed in the time since these data were collected. For example, Google Scholar lists the top publications in Criminology, Criminal Law, and Policing. Currently, The British Journal of Criminology, Justice Quarterly, Journal of Quantitative Criminology, Journal of Criminal Justice, and Criminology are listed as the top five publications, respectively. Even this ranking system can be disputed because when examining the impact factors for each of these journals, The British Journal of Criminology has an impact factor of 3.29, while Criminology has an impact factor of 6.67. Furthermore, the impact factor for Crime & Delinquency (2.31) implies that it may no longer be a “Big 5” journal, although it is still regarded as a top-tier outlet. In an era of “publish or perish,” and the over-valuing of publications in these high-impact journals in the tenure and promotion process, authors are likely following these trends and making increasing attempts to publish in these outlets, which will also impact the life of the overall publishing network.

Finally, while descriptive statistics were provided for the broader relationship between the journals themselves, the current study does not include a more in-depth analysis of the relationships and networks both within and between the Big 5 CCJ journals. There are several implications for publishing and publishing practices that can be identified through social network analysis that are not within the scope of the current study. For example, these journals include topics related to sub-disciplines, but author research interests were not collected to identify if this provides an explanation for the decentralization of the network. Even in a more specialized topic like experimental criminology (Fenimore et al., 2021), the network was largely decentralized, so the question still remains if there is a strong, core network of authors in CCJ.

Though not a limitation of the current study, additional demographic information should be collected from the authors that have published in these journals to better trace the invisible colleges that exist within CCJ and develop academic “genealogies” that would be useful in tracing the publication history of the field as a whole, as well as who is likely to have specialized in a particular topic. Ultimately, social network analysis provides a useful framework for illustrating the interrelationships of publishing networks and how individuals and groups contribute to the production of knowledge and science. Future research should continue to expand and analyze data from broader sets of journals including interdisciplinary outlets that are becoming more frequent outlets for CCJ research.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Danielle M. Fenimore is a research associate in the Center for Applied Research for the Police Executive Research Forum. Her research focuses on examining a variety of contemporary issues in policing, including crime prevention, procedural justice, investigative models, and police response to mass demonstrations. Her research also includes conducting evaluations of policing policies focused on improving modern policing practices related to crime prevention and community relationships.

Sean P. Roche is an assistant professor in the School of Criminal Justice and Criminology at Texas State University. In 2017, he received his PhD in Criminal Justice from the University at Albany, SUNY. His research interests include policing, public opinion about crime and criminal justice issues, and offender decision-making.

Wesley G. Jennings is Gillespie Distinguished Scholar, Chair, and Professor in the Department of Criminal Justice & Legal Studies in the School of Applied Sciences and a faculty affiliate at the School of Law at the University of Mississippi. His major research interests are quantitative methods, longitudinal data analysis, and experimental and quasi-experimental designs.

Remy Heinen is a master’s student in the Department of Criminal Justice & Legal Studies in the School of Applied Sciences at the University of Mississippi.

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