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Examining academic ranking and inequality in library and information science through faculty hiring networks
Journal of Informetrics 11 (2017) 641–654
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Journal of Informetrics journal homepage: www.elsevier.com/locate/joi
Regular article
Examining academic ranking and inequality in library and information science through faculty hiring networks Yongjun Zhu ∗ , Erjia Yan College of Computing and Informatics, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, United States
a r t i c l e
i n f o
Article history: Received 8 February 2017 Received in revised form 6 April 2017 Accepted 25 April 2017 Keywords: Library and information science Faculty hiring networks Placement LIS ranking Academic inequality
a b s t r a c t In this study, we examine academic ranking and inequality in library and information science (LIS) using a faculty hiring network of 643 faculty members from 44 LIS schools in the United States. We employ four groups of measures to study academic ranking, including adjacency, placement and hiring, distance-based measures, and hubs and authorities. Among these measures, closeness and hub measures have the highest correlation with the U.S. News ranking (r = 0.78). We study academic inequality using four distinct methods that include downward/upward placement, Lorenz curve, cliques, and egocentric networks of LIS schools and find that academic inequality exists in the LIS community. We show that the percentage of downward placement (68%) is much higher than that of upward placement (22%); meanwhile, 20% of the 30 LIS schools that have doctoral programs produced nearly 60% of all LIS faculty, with a Gini coefficient of 0.53. We also find cliques of highly ranked schools and a core/periphery structure that distinguishes LIS schools of different ranks. Overall, LIS faculty hiring networks have considerable value in deriving credible academic ranking and revealing faculty exchange within the field. © 2017 Elsevier Ltd. All rights reserved.
Introduction Faculty hiring networks, also known as faculty placement networks, are weighted networks of institutions that are interconnected by hiring relations. Faculty hiring networks have been examined at the department level to study relationships among universities within a discipline. Prior research has examined the knowledge diffusion aspect of hiring in disciplines such as sociology (e.g., Burris, 2004; Hanneman, 2001), political science (e.g., Fowler, Grofman, & Masuoka, 2007; Masuoka, Grofman, & Feld, 2007; Schmidt & Chingos, 2007), economics (e.g., Amir & Knauff, 2008; Terviö, 2011), communication science (e.g., Barnett, Danowski, Feeley, & Stalker, 2010 ; Barnett & Feeley, 2011; Mai, Liu, & González-Bailón, 2015), mathematics (e.g., Myers, Mucha, & Porter, 2011; Terviö, 2011), comparative literature (e.g., Terviö, 2011), law (e.g., Katz et al., 2011), computer science (e.g., Clauset, Arbesman, & Larremore, 2015), business science (e.g., Clauset et al., 2015), and history (e.g., Clauset et al., 2015). These studies have primarily focused on two tasks: examining prestige rankings through social network analyses of faculty hiring networks and probing into academic inequality of university departments. In library and information science (LIS), Sugimoto, Russell, & Grant (2009) examined the landscape of the field by analyzing dissertations conferred by 38 LIS schools in the United States and Canada from 1930 to 2007. More recently, Wiggins and Sawyer (2012) explored faculty hiring data of 21 iSchools. They investigated interdisciplinary diversity of iSchools and
∗ Corresponding author. E-mail addresses: [email protected] (Y. Zhu), [email protected] (E. Yan). http://dx.doi.org/10.1016/j.joi.2017.04.007 1751-1577/© 2017 Elsevier Ltd. All rights reserved.
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manually clustered them into four different groups (i.e., computational, library & information, sociotechnical, and niche) to understand the community. Because not all iSchools are departments of LIS, their study did not specifically discuss the disciplinary characteristics of LIS. In LIS, there are studies that examined the landscape and interdisciplinarity of the field by using dissertation or journal publication data. However, prestige rankings and academic inequality from the perspective of faculty hiring networks were rarely studied. Exploring these issues in LIS has vital importance as the outcomes will allow us to gain insights in the following areas: 1) the value of faculty hiring data on deriving objective LIS rankings; and 2) the revealing of academic inequality, if any, that hinders the growth of the field. Furthermore, this study provides additional understanding to the existing body of literature that enables cross-disciplinary comparisons and evaluations. In this study, we intend to investigate faculty hiring networks of 44 American Library Association (ALA)1 -accredited LIS schools ranked by U.S. News & World Report (U.S. News). Specifically, our goal is twofold. First, we examine network-based measures derived from faculty hiring networks to understand their implications on understanding LIS rankings. Second, we examine network structures of faculty hiring networks to understand academic inequality (if any) in LIS. 2. Literature review 2.1. Prestige rankings Prestige rankings of departments are regarded as an important research theme that directly relates to universities’ resource allocation, strategic planning, and decision making (Markovsky, 2000). Two leading ways of ranking departments are surveys and archival methods (Hanneman, 2001). Archival methods have been traditionally based on indicators such as citation counts and publication counts of faculty, and the amount of grant received (Fowler et al., 2007). However, these two methods are known to be either subjective or narrow, while ranking departments based on social network analysis of faculty hiring networks can capture not only the inherent relations among departments, but also qualitative aspects of the social hierarchy (Hanneman, 2001). A number of indicators from social network analysis have been applied to derive prestige rankings. Outdegree is the simplest indicator of measuring academic prestige by counting the total number of job placements of departments (e.g., Barnett & Feeley, 2011; Hanneman, 2001; Katz et al., 2011). Eigenvector centrality (Bonacich, 2007) is another popular approach which treats each connection differently by giving more weights to connections from high-scoring nodes. Thus, placements to high-prestige departments contribute more to the placing departments than placements to low-prestige departments (e.g., Amir & Knauff, 2008; Burris, 2004). In this context, PageRank, a popular variant of eigenvector, is used (e.g., Masuoka et al., 2007; Terviö, 2011). Studies have also used closeness centrality to identify departments that are important to the structure of networks in terms of having shorter distance to other departments in faculty hiring network (e.g., Barnett et al., 2010; Katz et al., 2011). Even though ranking results obtained by using above mentioned measures vary, they are generally correlated with rankings such as those provided by the National Research Council (NRC).2 or U.S. News & World Report (US News)3 For example, Hanneman (2001) reported that the correlation between degree centrality in Ph.D. placement and the NRC ranking is 0.85. Schmidt and Chingos (2007) also reported high correlations with NRC (r = 0.91) and US News (r = 0.93). While the above studies and measures primarily focused on faculty placement, a few studies (e.g., Katz et al., 2011; Fowler et al., 2007; Myers et al., 2011) examined prestige rankings from both placement and hiring perspectives by using Kleinberg’s hubs and authorities (1999). Hubs are departments that placed many graduates to other high-prestige departments and authorities are departments that hired faculty members from high prestige departments (Katz et al., 2011). Results showed that authority is a useful measure, in which higher authority is correlated with higher prestige (e.g., Masuoka et al., 2007; Myers et al., 2011). 2.2. Academic inequality Studying academic inequality is another important application of faculty hiring network analysis. Academic inequality in faculty hiring networks is expressed as the phenomenon that small portions of departments produced large portions of faculty and the existence of elitism, in which members of the elite groups hired graduates from each other. Burris (2004) found that hiring networks explained 84% of the variance in prestige rankings (compared to 50% explained by a combination of publications, citations, and grants). Burris (2004) reported that, in sociology, top five departments placed one-third of faculty to all 94 departments included in the study and the top 20 departments accounted for 70% of total hires. Likewise, Barnett and Feeley (2011) found that five high prestige communication science departments placed more than 20% of the faculty to 96 departments, and the top 10 departments accounted for nearly 35% of hires. DiRamio, Theroux, and Guarino (2009) discussed that, in higher education administration programs, 70% of the faculty at top-ranked departments are graduates of
1 2 3
http://www.ala.org/. http://sites.nationalacademies.org/PGA/Resdoc/. http://grad-schools.usnews.rankingsandreviews.com/best-graduate-schools.
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Table 1 Statistics of the LIS faculty hiring network. Item
Number
Nodes Edges Average degree Network diameter Graph density Modularity
44 246 11 6 0.13 0.22
other top-ranked programs. They also detected cliques that comprised top programs and had close connections with each other. Clauset, Arbesman, and Larremore (2015) identified academic hierarchies in three disciplines (i.e., computer science, business, and history) and found that only 9% to 14% of faculty were placed above their doctoral ranks; furthermore, 25% of the departments produced 71% to 86% of all faculty, with top 10 departments placed 1.6–3.0 times more faculty than the next 10 departments. Descriptive statistics (e.g., frequency) are easy-to-apply measures to study academic inequality (e.g., DiRamio, Theroux, & Guarino, 2009). The Gini coefficient is a useful measure (e.g., Barnett et al., 2010; Clauset et al., 2015), in which a low value denotes a higher equality and a high value denotes a higher inequality. In addition, clustering analysis is a method to identify cliques or elite groups of departments (e.g., Terviö, 2011). 3. Methods 3.1. Data We collected data on faculty members from 44 LIS schools ranked by U.S. News & World Report. In total, 642 faculty members who are working as a tenured or tenure track professors were included in our dataset. The faculty members with the earliest appointment year in our dataset was hired in 1975. Recently hired faculty members were included in our dataset as long as they were posted on their school website by the end of 2015. A detailed description is available in our previous study (Zhu, Yan, & Song, 2016). A directed, weighted, hiring network was constructed on the basis of faculty members who received their degrees from the 44 LIS schools. Table 1 provides statistics of the constructed network. As shown in Table 1, the network is relatively sparse with a graph density of 0.13. This could be attributed to its interdisciplinary characteristics, given that 44% of 642 faculty received Ph.D. in other disciplines based on the collected data. The low modularity (0.22) shows that the network has low likelihood to be divided into communities, which could be due to the size of the community. 3.2. Academic rankings The U.S. News ranking was used to benchmark the ranking results of network-based measures. The U.S. News ranking is obtained by surveying deans, program directors, and senior faculty members of LIS schools (Flanigan & Morse, 2015). There are several criticisms of this indicator; however, it remains the only ranking for LIS programs. Network-based measures are divided into the following four groups. 3.2.1. Adjacency Adjacency is measured by the number of distinct neighbors of a given node in a network. For a given LIS school in the faculty hiring network, adjacency measures the number of schools that the school has in contact. We examine two types of adjacency, i.e., out-adjacency for placement and in-adjacency for hiring. Two measures are expressed as: adjacency.out (i) =
n
Aij , Aij =
j=1
adjacency.in (i) =
n
Aji , Aji =
j=1
adjacency.out + in (i) =
n j=1
1ifFNetij ≥ 1, 0otherwise.
1ifFNetji ≥ 1, 0otherweise.
Aij + Aji , Aij =
1ifFNetij ≥ 1, 0otherwise.
andAji =
1ifFNetji ≥ 1, 0otherweise.
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where FNet is the faculty hiring network; n is the total number of LIS schools; and FNetij denotes the number of faculty that school j hired from school i. An LIS school with high adjacency denotes that the school has achieved a broad range of recognition in the community. 3.2.2. Placement and hiring Placement and hiring are measured by weighted degrees. Weighted outdegree measures the total placements that each LIS school made, and weighted indegree measures the total hires of each LIS school. LIS schools with high outdegrees are the ones that placed many of their own graduates into other schools, while schools with high indegree are the ones that hired many faculty from other LIS schools. Three measures based on placement and hiring are defined as follows: n
placement (i) =
FNetij
j=1
hiring (i) =
n
FNetji
j=1
placement + hiring (i) =
n
FNetij + FNetji
j=1
3.2.3. Distance-based centrality We examine academic rankings through two distance-based centralities: closeness centrality and betweenness centrality. Closeness centrality measures the mean distance from one node to all other nodes in a network (Boldi & Vigna, 2014). In the faculty hiring network, a school with a high closeness centrality denotes that the school is in a central position of connecting others in the network. Betweenness centrality measures the importance of nodes in a network in terms of bridging other nodes (Freeman, 1977). A node with a high betweenness centrality plays an important role in diffusing information throughout the network. Links in the faculty hiring network can be interpreted as knowledge trade in which faculty act as a medium. Thus, a school with a high betweenness centrality plays an influential role in knowledge diffusion within the network through the exchange of faculty members. We examine the two centralities in both directed and undirected manners. Two centralities can be calculate through: closeness (i) =
n
d j ij
betweenness (i) =
sp(i)jk j,k
spjk
where dij is the length of a geodesic distance from i to j; spjk is the total number of shortest paths from j to k; and sp(i)jk is the number of those paths that pass through i. 3.2.4. Hubs and authorities Eigenvector measures the importance of nodes in a network, in which links from high importance nodes contributes more than those from low importance nodes (Bonacich, 2007). Hubs and authorities are based on this concept, and each node has a hub and authority score (Kleinberg, 1999). Thus, in the faculty hiring network, hubs are schools that placed their graduates to other prestigious schools, and authorities are schools that hire faculty who graduated from prestigious schools. Because the study deals with LIS faculty hiring network, authorities in the study denote LIS schools’ “authorities” in the LIS faculty hiring network rather than the entire academic network. Like outdegree and indegree, hubs and authorities capture both aspects of placement and hiring. Hubs, authorities, and the integration of the two measures are defined as follows: hubs (i) =
FNetji xj
j
authorities (i) =
FNetij yj
j
hubs + authorities (i) =
j
FNetji xj + FNetij yj
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where xi is the authority centrality of i and yi is the hub centrality of i. 3.3. Academic inequality In order to understand academic inequality, structures of the whole network should be considered. We analyze academic inequality from the following aspects. 3.3.1. Downward and upward placement A school can place its graduates into other schools that are ranked both higher or lower than itself. A downward placement is the case in which higher ranked schools place graduates into lower ranked ones, while an upward placement is the opposite. Investigating downward and upward placement reveals the status of academic equality of communities of disciplines. 3.3.2. Lorenz curve and placement distribution A Lorenz curve (Gastwirth, 1971) is a measure that studies the inequality of wealth distribution in economics. It is represented as a curve drawn in a two-dimensional graph, in which the relationship between population and income is plotted. Along with a Lorenz curve, the Gini coefficient (Gastwirth, 1972) is another widely used measure of inequality. The Gini coefficient is a value between zero and one, in which zero represents perfect equality, and one expresses absolute inequality. In this study, a Lorenz curve is represented as a function between the cumulative fraction of faculty produced and the cumulative fraction of LIS schools that produced those faculty. Then, the Lorenz curve, along with the Gini coefficient, explains the distribution of placement. 3.3.3. Cliques of LIS schools A clique denotes a subgraph of a graph, in which every node is connected to every other node (Luce & Perry, 1949). In social network analysis, actors form cliques based on common characteristics and actors of the same clique express their intimate relationships (Wasserman & Faust, 1994). When identifying cliques, the faculty hiring network is treated as an undirected network, and we identify maximum/largest clique(s) and examine their membership from the perspective of different academic rankings. An equal faculty hiring network tends to contain cliques that combine schools of different ranks. Because directions in the faculty hiring network is not taken into account, cliques of solely highly or low ranked schools signify inequalities in hiring, represented as a phenomenon that lower ranked schools have difficulties in hiring graduates from higher ranked schools. 3.3.4. Egocentric networks of LIS schools An egocentric network is a type of networks in which all other nodes are directly connected to a focal node called ego (Borgatti et al., 2009). We visualize egocentric networks of LIS schools with different academic ranks and compare their structures. No structural differences among egocentric networks are expected in an equal faculty hiring networks, and notable structural differences would signify academic inequality. 4. Results 4.1. Results of academic rankings Fig. 1 shows Spearman’s rank correlations among three adjacency measures and the U.S. News ranking. The U.S. News rankings were flipped (i.e., the value 1 was converted to 44) to yield positive correlations. Out-adjacency (i.e., placement) reports a correlation of 0.73 with the U.S. News ranking while in-adjacency (i.e., hiring) does not have a high correlation (r = 0.43). A low correlation between out-adjacency and in-adjacency is also found (r = 0.2). This is expected because prestigious schools place their graduates into many other schools, but tend to hire graduates only from a small number of elite schools. We find that the sum of two adjacency values (adjacency.out + in in Fig. 1) has a correlation efficiency of 0.83 with the U.S. News ranking. By combining two measures, we consider LIS schools’ ability of both placing and hiring faculty, guided by the belief that prestigious schools should produce and hire faculty members to ensure sustainable development. Fig. 2 shows Spearman’s rank correlations among placement, hiring, and the U.S. News ranking. Placement reports a correlation of 0.73 with the U.S. News ranking, while hiring reports a low correlation (r = 0.53). The sum of placement and hiring has a correlation of 0.82 with the U.S. News ranking. The outcome is almost identical to that of adjacency measures. LIS schools vary in their sizes (i.e., the number of faculty members) and the size difference could be a factor affecting their placement and hiring because larger schools tend to have more sizable doctoral programs and produce more faculty members than smaller schools. They may also recruit more faculty members to maintain their school sizes. Therefore, we report ratios between placement/hiring and school size in Appendix C. Please note that only 30 schools that have doctoral programs (Appendix A) are shown. In Appendix C, schools have higher rankings in the U.S. News ranking tend to be those with larger sizes. However, we did not find high correlations between school size and placement (r = 0.21) or hiring (r = −0.44).
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Fig. 1. Spearman’s correlations among three adjacency measures and the U.S. News ranking.
Fig. 2. Spearman’s correlations among placement, hiring, and the U.S. News ranking.
Four distance-based measures (i.e., betweenness and closeness centrality in directed and undirected manners) are examined and the results are shown in Fig. 3. All four measures are highly correlated with the U.S. News ranking (i.e., greater than 0.7). While betweenness centrality in directed and undirected calculations yielded quite similar results, closeness centrality in the undirected network yielded higher correlation than that in the directed network. Because lower ranked schools tend not to produce a large amount of faculty, many lower ranked schools have very low or zero betweenness and closeness centrality in the directed network as shown in Fig. 3. Undirected networks remove the direction of links. In undirected networks, placement and hiring relationships between two schools are interpreted as their social ties. Closeness in the undirected network resulted in the highest correlation (r = 0.78) with the U.S. News ranking among all distance-based measures.
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Fig. 3. Spearman’s correlations among betweenness, closeness, and the U.S. News ranking.
Fig. 4. Spearman’s correlations among hub, authority scores, and the U.S. News ranking.
While adjacency, placement, and hiring consider only immediate neighbors of a given school, hubs and authorities measures the importance of a school by considering all links and their weights in a network (Fig. 4). Hub measure has a higher correlation (r = 0.78) than the authority measure (r = 0.58) with the U.S. News ranking. Integrating the two measures (hub + authority in Fig. 4) resulted in a correlation increase (i.e., from 0.78 to 0.79). By considering the overall network structure, both hub and authority measures resulted in a slight improvement over other measures: out-adjacency (r = 0.73), placement (r = 0.73), hiring (r = 0.53), and in-adjacency (r = 0.43). In total, we examined 10 single measures and three integrated measures. Overall, placement-based measures (outadjacency, placement, and hub) have higher correlations with the U.S. News ranking than hiring-based measures (in-adjacency, hiring, and authority). The result is partly due to the nature of hiring in LIS: on the one hand, LIS hires faculty members from a variety of disciplines; on the other hand, LIS mainly places its graduates within this discipline. Thus,
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Fig. 5. Downward vs. upward placement among 44 LIS schools. Table 2 Distribution of download and upward placements among four tiers.
Downward Upward
Tier one
Tier two
Tier three
Tier four
154 (69.4%) 27 (38.6%)
65 (29.3%) 27 (38.6%)
1 (0.4%) 8 (11.4%)
2 (0.9%) 8 (12.4%)
placement data better reflect LIS schools’ performances within this discipline. For this reason, placement is seen as a more accurate indicator than hiring in deriving LIS ranking using LIS faculty hiring network. 4.2. Results of academic inequality Fig. 5 shows downward and upward placements among 44 LIS schools. The 44 LIS schools were positioned based on their ranks (i.e., from rank 1 (the leftmost school) to rank 44 (the rightmost school). In addition, the 44 LIS schools were equally divided into four tiers (i.e., rank 1 ∼ 11, rank 12 ∼ 22, rank 23 ∼ 33, and rank 34 ∼ 44) and are color-coded. The top half of the figure denotes downward placement (i.e., placements from the schools at left to the schools at right) while the bottom half denotes upward placement (i.e., directions of the arcs are from right to the left). The size of each node is proportional to its placement (i.e., outdegree), and each school is represented by two nodes to signify its downward and upward placements. The width of the arcs is proportional to the size of placements between two schools. Fig. 5 illustrates that the distribution of faculty placement is highly skewed toward the downward side (i.e., 68% downward placement vs. 22% upward placement vs. 10% self-placement). It also shows that LIS schools of tiers three and four are not as competitive as schools in tiers one and two in producing LIS faculty, while they hired faculty mostly from schools of tier one and two. Table 2 shows the distribution of downward and upward placements among four tiers. LIS schools of tier one and tier two secured the most downward (98.7%) and upward (76%) placements. The result is not surprising because 14 out of 44 LIS schools do not have doctoral programs, and among them, 13 schools are included in tiers three and four (see Appendix A). Thus, among the 22 schools in tier three and tier four, only nine schools have or had (e.g., Texas Woman’s University LIS school has stopped accepting new doctoral students since 2013) doctoral programs. There are also a few exceptions. For example, Simmons College in tier one placed only one (the average number in tier one is 14) downward placement while SUNY Albany (tier three) placed seven (among total eight in tier three) upward placements. Performance of individual schools is analyzed based on the notion of expected upward and downward placements (Appendix D). Based on the rank of a school, we calculated two probabilities (i.e., upward and downward) that a school can place its graduates to other 43 schools. For example, Drexel University ranks 10th and has nine schools for upward placement and 34 schools for downward placement. Therefore, the school’s expected ratios for upward and downward placements are 0.21 and 0.79, respectively. We can then multiply the ratios with the total upward and downward placements (i.e., eight) to obtain the expected upward and downward placements (i.e., two and six, respectively). By comparing the difference between the actual
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Fig. 6. Distributions of faculty placement among 44 LIS schools. Table 3 Four maximum cliques in the faculty hiring network. Clique 1 UIUC (1) UNC (2) Washington (3) Syracuse (4) Drexel (10) FSU (13)
Clique 2
Clique 3
Clique 4
UIUC (1) UNC (2) Syracuse (4) Michigan (5) Rutgers (6) Texas (7)
UIUC (1) UNC (2) Syracuse (4) Rutgers (6) Drexel (10) FSU (13)
UIUC (1) UNC (2) Rutgers (6) Drexel (10) FSU (13) Milwaukee (15)
and the expected upward placements, we can tell an individual school’s performance. Based on this statistic (as shown in the parentheses), schools such as Syracuse (4), UCLA (3), Michigan (2), Albany (2), Washington (1), Rutgers (1), Indiana (1), Maryland (1), Pittsburg (1), and Alabama (1) performed better than expected. On the other hand, schools such as FSU (-2), UNT (-2), Milwaukee (-1), Madison (-1), SC (-1), Hawaii (-1), and Arizona (-1) performed slightly worse than the expected. The inequality in faculty hiring is manifested by three facts: first, the size of downward placement is much greater than that of upward placement; second, among downward placements, those made by highly ranked LIS schools accounted for a greater portion; third, among upward placements, most placements were secured by highly ranked LIS schools. Taking a step further, the academic inequality in faculty placement is also shown in the analysis of the relationship between placements and the number of LIS schools that made the placements (Fig. 6). In the analysis we only included LIS schools with doctoral programs. It shows that 20% of 30 LIS schools produced nearly 60% of all LIS faculty. The Gini coefficient of the faculty hiring network is 0.53, which denotes a strong inequality in faculty placement. The value is lower than that of other disciplines reported in previous studies, in which, Gini coefficients of communication science, computer science, business, and history are 0.72, 0.69, 0.62, and 0.72, respectively (Barnett et al., 2010; Clauset et al., 2015). Inequality is also apparent in the number of faculty produced by each LIS school (Fig. 6B). Only about 20% of LIS schools have placed more than 15 faculty members while more than 45% of schools have placed less than five faculty members into other LIS schools based on the data we collected. It is possible that the actual number would be greater than five, given that out data does not include retired faculty. Nevertheless, the differences among LIS schools in faculty placement are apparent. Inequality is also found in faculty hiring in addition to faculty placement. Identified maximum cliques (Table 3) in the faculty hiring network shows inequality exists in faculty hiring. We identified four maximum cliques with the size of six. The numbers in parentheses denote its rank. All LIS schools in Table 3 are ranked 15 or higher. It is noticeable that lower ranked schools have limited access to a broad range of LIS schools in terms of faculty hiring. Comparisons of egocentric networks of LIS schools with different ranks also reveal academic inequality. Fig. 7 shows the egocentric networks of four LIS schools with different ranks (i.e., rank 1, rank 12, rank 23, and rank 34). The figure illustrates that the number of neighbors of each ego is decreasing along with dropping ranks. It is also clear that they differ in density. A core/periphery structure is identified in Fig. 7, in which their coreness scores (Borgatti & Everett, 2000) are 0.353, 0.194, 0.035, and 0.023, respectively. Thus, top schools are positioned at the core of the network (e.g., UIUC) while low ranked schools are located in the periphery (e.g., Greensboro). This signifies that the core/periphery structure in the faculty hiring network is closely related to academic inequality in LIS. Overall, academic inequality exists in both placement and hiring: 1) higher-ranked schools actively place their graduates to lower-ranked schools while tend to hire from schools with similar ranks; 2) lower-ranked schools rarely place their graduates to higher-ranked schools and tend to hire from schools similar to their rankings.
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Fig. 7. Egocentric networks of four LIS schools with different ranks (one from each tier).
5. Discussion and conclusion In this paper, we studied academic ranking and inequality in LIS by examining LIS faculty hiring networks. Forty-four LIS schools ranked by U.S. News were selected, and their hiring/placement relations were captured to build a LIS faculty hiring network. We evaluated four groups of network-based measures and their relationships with the U.S. News ranking that include adjacency, placement and hiring, distance-based measures, and hubs and authorities. Findings showed that, placement-based measures have higher correlations with the U.S. News ranking than hiring-based measures. The results demonstrated that LIS schools’ ability of placing graduates aligns more closely with their U.S. News rankings than their ability of hiring graduates. As a single measure, closeness (undirected) and the hub measure had the highest correlation with the U.S. News ranking (both had r = 0.78). Among integrated measures, adjacency (out + in) had the highest correlation (r = 0.83), followed by an integrated measure of placement and hiring (r = 0.82). In the study of LIS faculty placement inequality, we found that the percentage of downward placement (68%) is much higher than that of upward placement (22%). Meanwhile, results showed that 20% of the 30 LIS schools that have doctoral programs produced nearly 60% of all LIS faculty, with a Gini coefficient of 0.53. The value is lower than that of other disciplines reported in previous studies. However, the value still denotes a strong inequality in faculty placement. We also found four maximum cliques that comprised only highly ranked schools. A core/periphery structure was identified in the network, in which highly ranked schools positioned themselves in the core of the network while low ranked schools are in the periphery. The study has a few limitations. As previously mentioned, among the 44 LIS schools, only 30 schools have doctoral programs. Therefore, schools without doctoral programs were unrepresented in measures such as out-adjacency and placement. Given that out-degree-based measures (i.e., out-adjacency, placement, and hub) have higher correlations with the U.S. News ranking than in-degree-based measures (i.e., in-adjacency, hiring, and authority), the existence of doctoral programs plays an important role in LIS schools ranking. As described in our previous study (Zhu, Yan, & Song, 2016), 60% of LIS faculty members received doctoral degrees in LIS and this share has declined in favor of Computer Science graduates. In a way, this is a limitation of the current methods, in that it focused on LIS programs, even though we are moving to a point where the majority of faculty might be from outside the field—we need to reconceptualize what this field is—yet, this reconceptualization is beyond the scope of this study. Another limitation is that the study only explored faculty members who received doctoral degrees in American LIS schools and excluded international programs. However, this issue was mitigated given that 90% of faculty members received doctoral degrees in the U.S. (Zhu et al., 2016). Due to the above limitations, we had to study the problem with a partial dataset. For example, several schools such as University of Pittsburg, University of Arizona, Clarion University of Pennsylvania, Syracuse University, University of Texas at Austin, and University of Michigan have fewer than 40% of faculty members who graduated from LIS programs. This partial dataset may affect the analytical results made in the article. Another limitation is that the analysis in the study is static (i.e., current faculty) and does not show the evolution of
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LIS faculty hiring over time. Last, we tackled academic ranking and inequality only from the perspective of faculty hiring; however, academic careers are not the only career options for LIS graduates, and the results and conclusions may be different if we consider non-academic career options. The high correlations between network measures derived from the LIS faculty hiring network and the U.S. News ranking show the value of LIS faculty hiring network in understanding LIS academic ranking and can be used as an effective measure to rank LIS schools. While the current LIS ranking performed by U.S. News is a time-consuming process that involves much human involvement (e.g., surveys), the introduced measures can efficiently supplement the U.S. News ranking by enabling efficient and interpretable results. A concern out of these LIS rankings is whether they are actually associated with, and result in academic inequality. Unfortunately, we did observe academic inequality exists in the LIS community, from the aspect of faculty hiring and placement, through a LIS faculty hiring network. We believe that the results can be used to initiate candid conversation on ways to reduce academic inequality. In this regard, we expect future studies would contribute to the issue. Authors contributions Yongjun Zhu: Conceived and designed the analysis, Collected the data, Contributed data or analysis tools, Performed the analysis, Wrote the paper. Erjia Yan: Collected the data, Contributed data or analysis tools, Wrote the paper. Acknowledgments We thank Dr. Cassidy R. Sugimoto of Indiana University for her valuable feedback on an earlier version of this paper. This project was made possible in part by the Institute of Museum and Library Services (Grant Award Number: RE-07-15-006015), for the project titled “Building an entity-based research framework to enhance digital services on knowledge discovery and delivery”. Appendix A. The list of 44 LIS schools U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Institution
Abbreviation
Doctoral program
Placement
Hiring
University of Illinois at Urbana–Champaign University of North Carolina at Chapel Hill University of Washington Syracuse University University of Michigan Rutgers University University of Texas at Austin Indiana University Bloomington Simmons College Drexel University University of Maryland, College Park University of Pittsburgh Florida State University University of California, Los Angeles University of Wisconsin-Milwaukee University of Wisconsin-Madison University of Tennessee Kent State University University of Alabama University of South Carolina University of North Texas University of Kentucky University of North Carolina at Greensboro University of Oklahoma University of South Florida Wayne State University Catholic University of America Dominican University Louisiana State University University of Hawaii Pratt Institute SUNY Albany San Jose State University University of Arizona University of Denver University of Iowa University of Missouri Texas Woman’s University
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Kentucky Greensboro Oklahoma USF Wayne CUA Dominican LSU Hawaii Pratt Albany SJSU Arizona Denver Iowa Missouri TWU
Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N N Y N N N Y N Y N Y N Y N Y Y Y
33 31 7 26 17 34 13 14 2 9 8 30 20 15 4 11 3 1 3 2 14 0 0 1 0 0 0 0 0 1 0 7 0 4 0 0 7 4
10 15 9 10 6 7 6 6 16 13 8 6 12 4 12 3 10 11 9 10 14 8 6 8 7 4 6 6 6 4 4 4 9 6 4 3 6 9
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39 40 41 42 43 44
LIU Post SUNY Buffalo City University of New York North Carolina Central University Clarion University of Pennsylvania St. John’s University
LIU Buffalo CUNY NCCU Clarion SJU
Y N N N N N
3 0 0 0 0 0
5 3 8 7 2 3
Appendix B. Ranks of the 44 LIS schools in the employed measures U.S. News 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Institution
A(O)
A(I)
A
P
H
P+H
B(D)
B(U)
C(D)
C(U)
HU
AU
HU+AU
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Kentucky Greensboro Oklahoma USF Wayne CUA Dominican LSU Hawaii Pratt Albany SJSU Arizona Denver Iowa Missouri TWU LIU Buffalo CUNY NCCU Clarion SJU
4 2 16 4 6 1 11 9 22 13 14 3 6 6 18 11 20 26 22 22 10 30 30 26 30 30 30 30 30 26 30 14 30 22 30 30 17 18 20 26 30 30 30 30
10 1 15 15 27 15 23 27 2 5 23 36 5 36 5 36 10 2 15 5 4 10 15 15 10 27 23 27 15 27 36 27 5 27 27 36 27 10 23 36 36 15 44 36
3 1 14 4 8 2 12 9 14 9 14 5 5 9 14 13 21 18 24 21 7 27 30 27 27 36 34 36 30 34 40 18 24 30 36 40 23 18 24 36 40 30 44 40
2 3 15 5 7 1 11 9 24 13 14 4 6 8 18 12 21 26 21 24 9 30 30 26 30 30 30 30 30 26 30 15 30 18 30 30 15 18 21 26 30 30 30 30
8 2 12 8 23 20 23 23 1 4 16 23 5 34 5 40 8 7 12 8 3 16 23 16 20 34 23 23 23 34 34 34 12 23 34 40 23 12 33 40 16 20 44 40
2 1 14 4 8 3 11 10 13 9 14 4 6 11 14 17 18 21 21 21 7 28 33 26 31 38 33 33 33 37 38 24 26 25 38 42 18 18 28 38 28 31 44 42
12 1 10 5 11 4 13 17 21 20 16 7 8 6 19 18 14 23 24 15 3 29 29 9 29 29 29 29 29 28 29 2 29 25 29 29 22 26 27 29 29 29 29 29
14 2 32 8 11 1 10 4 21 12 19 6 7 5 17 9 16 18 31 15 3 23 33 30 24 34 36 43 29 35 44 13 25 27 37 38 20 22 28 40 42 26 41 39
12 4 10 3 2 1 7 9 28 8 14 6 12 5 20 17 22 26 21 27 15 30 30 19 30 30 30 30 30 28 30 11 30 23 30 30 24 25 18 16 30 30 30 30
14 1 19 3 4 2 10 6 24 11 19 6 6 6 19 11 19 11 31 17 4 25 15 34 18 36 29 41 28 31 44 15 26 39 37 35 31 19 26 37 42 29 42 40
1 3 13 5 7 2 12 9 21 11 14 4 6 8 22 15 18 27 19 29 10 32 32 24 32 32 31 32 32 28 40 16 30 23 32 40 17 20 26 25 40 32 40 40
9 7 12 5 28 19 18 21 1 6 23 17 3 40 2 39 11 10 13 14 4 25 22 16 30 42 26 27 34 38 29 33 20 32 41 35 36 15 31 37 8 24 43 44
1 2 11 3 15 5 14 12 8 9 17 6 4 21 10 26 16 18 19 22 7 30 25 23 36 42 31 32 38 39 33 27 24 35 41 40 28 20 34 37 13 29 43 44
1. A(O): Adjacency. out. 2. A(I): Adjacency.in. 3. A: Adjacency.out + in. 4. P: Placement. 5. H: Hiring. 6. P + H: Placement + Hiring. 7. B(D): Betweenness.directed. 8. B(U): Betweenness.undirected. 9. C(D): Closeness.directed. 10. C(U): Closeness.undirected. 11. HU: Hub. 12. AU: Authority. 13. HU + AU: Hub + Authority.
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Appendix C. Ranks of the 30 LIS schools measured by the ratio between placement/hiring and school size U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 28 30 32 34 36 37 38 39
Institution
Rank (school size)
Rank (placement/size)
Rank (hiring/size)
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Oklahoma Dominican Hawaii Albany Arizona Iowa Missouri TWU LIU
5 7 2 3 1 16 12 17 11 4 9 8 10 20 6 27 18 14 23 19 13 24 21 28 29 15 30 25 22 26
5 4 21 10 14 1 12 7 26 18 15 3 8 6 23 2 22 28 19 24 11 27 29 25 9 20 30 13 16 17
22 9 28 25 30 18 26 20 1 19 24 29 10 27 17 21 6 8 2 7 3 5 16 12 13 23 11 15 4 14
Appendix D. Individual school’s performance in upward and downward placements (30 schools with doctoral programs) U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 28 30 32 34 36 37 38 39
Institution
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Oklahoma Dominican Hawaii Albany Arizona Iowa Missouri TWU LIU
Total w/o selfplacement 31 30 6 23 14 33 13 14 1 8 8 26 18 15 4 11 3 1 1 2 11 1 0 1 7 1 0 4 3 2
Expected upplacement 0 1 0 2 1 4 2 2 0 2 2 7 5 5 1 4 1 0 0 1 5 1 0 1 5 1 0 3 3 2
Expected downplacement 31 29 6 21 13 29 11 12 1 6 6 19 13 10 3 7 2 1 1 1 6 0 0 0 2 0 0 1 0 0
Actual upplacement 0 1 1 6 3 5 3 3 0 2 3 8 3 8 0 3 1 0 1 0 3 1 0 0 7 0 0 3 3 2
Actual downplacement 31 29 5 17 11 28 10 11 1 6 5 18 15 7 4 8 2 1 0 2 8 0 0 1 0 1 0 1 0 0
Difference in up-placement (actual-expected) 0 0 1 4 2 1 1 1 0 0 1 1 1-2 3 -1 -1 0 0 1 -1 -2 0 0 -1 2 -1 0 0 0 0
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Regular article
Examining academic ranking and inequality in library and information science through faculty hiring networks Yongjun Zhu ∗ , Erjia Yan College of Computing and Informatics, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, United States
a r t i c l e
i n f o
Article history: Received 8 February 2017 Received in revised form 6 April 2017 Accepted 25 April 2017 Keywords: Library and information science Faculty hiring networks Placement LIS ranking Academic inequality
a b s t r a c t In this study, we examine academic ranking and inequality in library and information science (LIS) using a faculty hiring network of 643 faculty members from 44 LIS schools in the United States. We employ four groups of measures to study academic ranking, including adjacency, placement and hiring, distance-based measures, and hubs and authorities. Among these measures, closeness and hub measures have the highest correlation with the U.S. News ranking (r = 0.78). We study academic inequality using four distinct methods that include downward/upward placement, Lorenz curve, cliques, and egocentric networks of LIS schools and find that academic inequality exists in the LIS community. We show that the percentage of downward placement (68%) is much higher than that of upward placement (22%); meanwhile, 20% of the 30 LIS schools that have doctoral programs produced nearly 60% of all LIS faculty, with a Gini coefficient of 0.53. We also find cliques of highly ranked schools and a core/periphery structure that distinguishes LIS schools of different ranks. Overall, LIS faculty hiring networks have considerable value in deriving credible academic ranking and revealing faculty exchange within the field. © 2017 Elsevier Ltd. All rights reserved.
Introduction Faculty hiring networks, also known as faculty placement networks, are weighted networks of institutions that are interconnected by hiring relations. Faculty hiring networks have been examined at the department level to study relationships among universities within a discipline. Prior research has examined the knowledge diffusion aspect of hiring in disciplines such as sociology (e.g., Burris, 2004; Hanneman, 2001), political science (e.g., Fowler, Grofman, & Masuoka, 2007; Masuoka, Grofman, & Feld, 2007; Schmidt & Chingos, 2007), economics (e.g., Amir & Knauff, 2008; Terviö, 2011), communication science (e.g., Barnett, Danowski, Feeley, & Stalker, 2010 ; Barnett & Feeley, 2011; Mai, Liu, & González-Bailón, 2015), mathematics (e.g., Myers, Mucha, & Porter, 2011; Terviö, 2011), comparative literature (e.g., Terviö, 2011), law (e.g., Katz et al., 2011), computer science (e.g., Clauset, Arbesman, & Larremore, 2015), business science (e.g., Clauset et al., 2015), and history (e.g., Clauset et al., 2015). These studies have primarily focused on two tasks: examining prestige rankings through social network analyses of faculty hiring networks and probing into academic inequality of university departments. In library and information science (LIS), Sugimoto, Russell, & Grant (2009) examined the landscape of the field by analyzing dissertations conferred by 38 LIS schools in the United States and Canada from 1930 to 2007. More recently, Wiggins and Sawyer (2012) explored faculty hiring data of 21 iSchools. They investigated interdisciplinary diversity of iSchools and
∗ Corresponding author. E-mail addresses: [email protected] (Y. Zhu), [email protected] (E. Yan). http://dx.doi.org/10.1016/j.joi.2017.04.007 1751-1577/© 2017 Elsevier Ltd. All rights reserved.
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manually clustered them into four different groups (i.e., computational, library & information, sociotechnical, and niche) to understand the community. Because not all iSchools are departments of LIS, their study did not specifically discuss the disciplinary characteristics of LIS. In LIS, there are studies that examined the landscape and interdisciplinarity of the field by using dissertation or journal publication data. However, prestige rankings and academic inequality from the perspective of faculty hiring networks were rarely studied. Exploring these issues in LIS has vital importance as the outcomes will allow us to gain insights in the following areas: 1) the value of faculty hiring data on deriving objective LIS rankings; and 2) the revealing of academic inequality, if any, that hinders the growth of the field. Furthermore, this study provides additional understanding to the existing body of literature that enables cross-disciplinary comparisons and evaluations. In this study, we intend to investigate faculty hiring networks of 44 American Library Association (ALA)1 -accredited LIS schools ranked by U.S. News & World Report (U.S. News). Specifically, our goal is twofold. First, we examine network-based measures derived from faculty hiring networks to understand their implications on understanding LIS rankings. Second, we examine network structures of faculty hiring networks to understand academic inequality (if any) in LIS. 2. Literature review 2.1. Prestige rankings Prestige rankings of departments are regarded as an important research theme that directly relates to universities’ resource allocation, strategic planning, and decision making (Markovsky, 2000). Two leading ways of ranking departments are surveys and archival methods (Hanneman, 2001). Archival methods have been traditionally based on indicators such as citation counts and publication counts of faculty, and the amount of grant received (Fowler et al., 2007). However, these two methods are known to be either subjective or narrow, while ranking departments based on social network analysis of faculty hiring networks can capture not only the inherent relations among departments, but also qualitative aspects of the social hierarchy (Hanneman, 2001). A number of indicators from social network analysis have been applied to derive prestige rankings. Outdegree is the simplest indicator of measuring academic prestige by counting the total number of job placements of departments (e.g., Barnett & Feeley, 2011; Hanneman, 2001; Katz et al., 2011). Eigenvector centrality (Bonacich, 2007) is another popular approach which treats each connection differently by giving more weights to connections from high-scoring nodes. Thus, placements to high-prestige departments contribute more to the placing departments than placements to low-prestige departments (e.g., Amir & Knauff, 2008; Burris, 2004). In this context, PageRank, a popular variant of eigenvector, is used (e.g., Masuoka et al., 2007; Terviö, 2011). Studies have also used closeness centrality to identify departments that are important to the structure of networks in terms of having shorter distance to other departments in faculty hiring network (e.g., Barnett et al., 2010; Katz et al., 2011). Even though ranking results obtained by using above mentioned measures vary, they are generally correlated with rankings such as those provided by the National Research Council (NRC).2 or U.S. News & World Report (US News)3 For example, Hanneman (2001) reported that the correlation between degree centrality in Ph.D. placement and the NRC ranking is 0.85. Schmidt and Chingos (2007) also reported high correlations with NRC (r = 0.91) and US News (r = 0.93). While the above studies and measures primarily focused on faculty placement, a few studies (e.g., Katz et al., 2011; Fowler et al., 2007; Myers et al., 2011) examined prestige rankings from both placement and hiring perspectives by using Kleinberg’s hubs and authorities (1999). Hubs are departments that placed many graduates to other high-prestige departments and authorities are departments that hired faculty members from high prestige departments (Katz et al., 2011). Results showed that authority is a useful measure, in which higher authority is correlated with higher prestige (e.g., Masuoka et al., 2007; Myers et al., 2011). 2.2. Academic inequality Studying academic inequality is another important application of faculty hiring network analysis. Academic inequality in faculty hiring networks is expressed as the phenomenon that small portions of departments produced large portions of faculty and the existence of elitism, in which members of the elite groups hired graduates from each other. Burris (2004) found that hiring networks explained 84% of the variance in prestige rankings (compared to 50% explained by a combination of publications, citations, and grants). Burris (2004) reported that, in sociology, top five departments placed one-third of faculty to all 94 departments included in the study and the top 20 departments accounted for 70% of total hires. Likewise, Barnett and Feeley (2011) found that five high prestige communication science departments placed more than 20% of the faculty to 96 departments, and the top 10 departments accounted for nearly 35% of hires. DiRamio, Theroux, and Guarino (2009) discussed that, in higher education administration programs, 70% of the faculty at top-ranked departments are graduates of
1 2 3
http://www.ala.org/. http://sites.nationalacademies.org/PGA/Resdoc/. http://grad-schools.usnews.rankingsandreviews.com/best-graduate-schools.
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Table 1 Statistics of the LIS faculty hiring network. Item
Number
Nodes Edges Average degree Network diameter Graph density Modularity
44 246 11 6 0.13 0.22
other top-ranked programs. They also detected cliques that comprised top programs and had close connections with each other. Clauset, Arbesman, and Larremore (2015) identified academic hierarchies in three disciplines (i.e., computer science, business, and history) and found that only 9% to 14% of faculty were placed above their doctoral ranks; furthermore, 25% of the departments produced 71% to 86% of all faculty, with top 10 departments placed 1.6–3.0 times more faculty than the next 10 departments. Descriptive statistics (e.g., frequency) are easy-to-apply measures to study academic inequality (e.g., DiRamio, Theroux, & Guarino, 2009). The Gini coefficient is a useful measure (e.g., Barnett et al., 2010; Clauset et al., 2015), in which a low value denotes a higher equality and a high value denotes a higher inequality. In addition, clustering analysis is a method to identify cliques or elite groups of departments (e.g., Terviö, 2011). 3. Methods 3.1. Data We collected data on faculty members from 44 LIS schools ranked by U.S. News & World Report. In total, 642 faculty members who are working as a tenured or tenure track professors were included in our dataset. The faculty members with the earliest appointment year in our dataset was hired in 1975. Recently hired faculty members were included in our dataset as long as they were posted on their school website by the end of 2015. A detailed description is available in our previous study (Zhu, Yan, & Song, 2016). A directed, weighted, hiring network was constructed on the basis of faculty members who received their degrees from the 44 LIS schools. Table 1 provides statistics of the constructed network. As shown in Table 1, the network is relatively sparse with a graph density of 0.13. This could be attributed to its interdisciplinary characteristics, given that 44% of 642 faculty received Ph.D. in other disciplines based on the collected data. The low modularity (0.22) shows that the network has low likelihood to be divided into communities, which could be due to the size of the community. 3.2. Academic rankings The U.S. News ranking was used to benchmark the ranking results of network-based measures. The U.S. News ranking is obtained by surveying deans, program directors, and senior faculty members of LIS schools (Flanigan & Morse, 2015). There are several criticisms of this indicator; however, it remains the only ranking for LIS programs. Network-based measures are divided into the following four groups. 3.2.1. Adjacency Adjacency is measured by the number of distinct neighbors of a given node in a network. For a given LIS school in the faculty hiring network, adjacency measures the number of schools that the school has in contact. We examine two types of adjacency, i.e., out-adjacency for placement and in-adjacency for hiring. Two measures are expressed as: adjacency.out (i) =
n
Aij , Aij =
j=1
adjacency.in (i) =
n
Aji , Aji =
j=1
adjacency.out + in (i) =
n j=1
1ifFNetij ≥ 1, 0otherwise.
1ifFNetji ≥ 1, 0otherweise.
Aij + Aji , Aij =
1ifFNetij ≥ 1, 0otherwise.
andAji =
1ifFNetji ≥ 1, 0otherweise.
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where FNet is the faculty hiring network; n is the total number of LIS schools; and FNetij denotes the number of faculty that school j hired from school i. An LIS school with high adjacency denotes that the school has achieved a broad range of recognition in the community. 3.2.2. Placement and hiring Placement and hiring are measured by weighted degrees. Weighted outdegree measures the total placements that each LIS school made, and weighted indegree measures the total hires of each LIS school. LIS schools with high outdegrees are the ones that placed many of their own graduates into other schools, while schools with high indegree are the ones that hired many faculty from other LIS schools. Three measures based on placement and hiring are defined as follows: n
placement (i) =
FNetij
j=1
hiring (i) =
n
FNetji
j=1
placement + hiring (i) =
n
FNetij + FNetji
j=1
3.2.3. Distance-based centrality We examine academic rankings through two distance-based centralities: closeness centrality and betweenness centrality. Closeness centrality measures the mean distance from one node to all other nodes in a network (Boldi & Vigna, 2014). In the faculty hiring network, a school with a high closeness centrality denotes that the school is in a central position of connecting others in the network. Betweenness centrality measures the importance of nodes in a network in terms of bridging other nodes (Freeman, 1977). A node with a high betweenness centrality plays an important role in diffusing information throughout the network. Links in the faculty hiring network can be interpreted as knowledge trade in which faculty act as a medium. Thus, a school with a high betweenness centrality plays an influential role in knowledge diffusion within the network through the exchange of faculty members. We examine the two centralities in both directed and undirected manners. Two centralities can be calculate through: closeness (i) =
n
d j ij
betweenness (i) =
sp(i)jk j,k
spjk
where dij is the length of a geodesic distance from i to j; spjk is the total number of shortest paths from j to k; and sp(i)jk is the number of those paths that pass through i. 3.2.4. Hubs and authorities Eigenvector measures the importance of nodes in a network, in which links from high importance nodes contributes more than those from low importance nodes (Bonacich, 2007). Hubs and authorities are based on this concept, and each node has a hub and authority score (Kleinberg, 1999). Thus, in the faculty hiring network, hubs are schools that placed their graduates to other prestigious schools, and authorities are schools that hire faculty who graduated from prestigious schools. Because the study deals with LIS faculty hiring network, authorities in the study denote LIS schools’ “authorities” in the LIS faculty hiring network rather than the entire academic network. Like outdegree and indegree, hubs and authorities capture both aspects of placement and hiring. Hubs, authorities, and the integration of the two measures are defined as follows: hubs (i) =
FNetji xj
j
authorities (i) =
FNetij yj
j
hubs + authorities (i) =
j
FNetji xj + FNetij yj
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where xi is the authority centrality of i and yi is the hub centrality of i. 3.3. Academic inequality In order to understand academic inequality, structures of the whole network should be considered. We analyze academic inequality from the following aspects. 3.3.1. Downward and upward placement A school can place its graduates into other schools that are ranked both higher or lower than itself. A downward placement is the case in which higher ranked schools place graduates into lower ranked ones, while an upward placement is the opposite. Investigating downward and upward placement reveals the status of academic equality of communities of disciplines. 3.3.2. Lorenz curve and placement distribution A Lorenz curve (Gastwirth, 1971) is a measure that studies the inequality of wealth distribution in economics. It is represented as a curve drawn in a two-dimensional graph, in which the relationship between population and income is plotted. Along with a Lorenz curve, the Gini coefficient (Gastwirth, 1972) is another widely used measure of inequality. The Gini coefficient is a value between zero and one, in which zero represents perfect equality, and one expresses absolute inequality. In this study, a Lorenz curve is represented as a function between the cumulative fraction of faculty produced and the cumulative fraction of LIS schools that produced those faculty. Then, the Lorenz curve, along with the Gini coefficient, explains the distribution of placement. 3.3.3. Cliques of LIS schools A clique denotes a subgraph of a graph, in which every node is connected to every other node (Luce & Perry, 1949). In social network analysis, actors form cliques based on common characteristics and actors of the same clique express their intimate relationships (Wasserman & Faust, 1994). When identifying cliques, the faculty hiring network is treated as an undirected network, and we identify maximum/largest clique(s) and examine their membership from the perspective of different academic rankings. An equal faculty hiring network tends to contain cliques that combine schools of different ranks. Because directions in the faculty hiring network is not taken into account, cliques of solely highly or low ranked schools signify inequalities in hiring, represented as a phenomenon that lower ranked schools have difficulties in hiring graduates from higher ranked schools. 3.3.4. Egocentric networks of LIS schools An egocentric network is a type of networks in which all other nodes are directly connected to a focal node called ego (Borgatti et al., 2009). We visualize egocentric networks of LIS schools with different academic ranks and compare their structures. No structural differences among egocentric networks are expected in an equal faculty hiring networks, and notable structural differences would signify academic inequality. 4. Results 4.1. Results of academic rankings Fig. 1 shows Spearman’s rank correlations among three adjacency measures and the U.S. News ranking. The U.S. News rankings were flipped (i.e., the value 1 was converted to 44) to yield positive correlations. Out-adjacency (i.e., placement) reports a correlation of 0.73 with the U.S. News ranking while in-adjacency (i.e., hiring) does not have a high correlation (r = 0.43). A low correlation between out-adjacency and in-adjacency is also found (r = 0.2). This is expected because prestigious schools place their graduates into many other schools, but tend to hire graduates only from a small number of elite schools. We find that the sum of two adjacency values (adjacency.out + in in Fig. 1) has a correlation efficiency of 0.83 with the U.S. News ranking. By combining two measures, we consider LIS schools’ ability of both placing and hiring faculty, guided by the belief that prestigious schools should produce and hire faculty members to ensure sustainable development. Fig. 2 shows Spearman’s rank correlations among placement, hiring, and the U.S. News ranking. Placement reports a correlation of 0.73 with the U.S. News ranking, while hiring reports a low correlation (r = 0.53). The sum of placement and hiring has a correlation of 0.82 with the U.S. News ranking. The outcome is almost identical to that of adjacency measures. LIS schools vary in their sizes (i.e., the number of faculty members) and the size difference could be a factor affecting their placement and hiring because larger schools tend to have more sizable doctoral programs and produce more faculty members than smaller schools. They may also recruit more faculty members to maintain their school sizes. Therefore, we report ratios between placement/hiring and school size in Appendix C. Please note that only 30 schools that have doctoral programs (Appendix A) are shown. In Appendix C, schools have higher rankings in the U.S. News ranking tend to be those with larger sizes. However, we did not find high correlations between school size and placement (r = 0.21) or hiring (r = −0.44).
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Fig. 1. Spearman’s correlations among three adjacency measures and the U.S. News ranking.
Fig. 2. Spearman’s correlations among placement, hiring, and the U.S. News ranking.
Four distance-based measures (i.e., betweenness and closeness centrality in directed and undirected manners) are examined and the results are shown in Fig. 3. All four measures are highly correlated with the U.S. News ranking (i.e., greater than 0.7). While betweenness centrality in directed and undirected calculations yielded quite similar results, closeness centrality in the undirected network yielded higher correlation than that in the directed network. Because lower ranked schools tend not to produce a large amount of faculty, many lower ranked schools have very low or zero betweenness and closeness centrality in the directed network as shown in Fig. 3. Undirected networks remove the direction of links. In undirected networks, placement and hiring relationships between two schools are interpreted as their social ties. Closeness in the undirected network resulted in the highest correlation (r = 0.78) with the U.S. News ranking among all distance-based measures.
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Fig. 3. Spearman’s correlations among betweenness, closeness, and the U.S. News ranking.
Fig. 4. Spearman’s correlations among hub, authority scores, and the U.S. News ranking.
While adjacency, placement, and hiring consider only immediate neighbors of a given school, hubs and authorities measures the importance of a school by considering all links and their weights in a network (Fig. 4). Hub measure has a higher correlation (r = 0.78) than the authority measure (r = 0.58) with the U.S. News ranking. Integrating the two measures (hub + authority in Fig. 4) resulted in a correlation increase (i.e., from 0.78 to 0.79). By considering the overall network structure, both hub and authority measures resulted in a slight improvement over other measures: out-adjacency (r = 0.73), placement (r = 0.73), hiring (r = 0.53), and in-adjacency (r = 0.43). In total, we examined 10 single measures and three integrated measures. Overall, placement-based measures (outadjacency, placement, and hub) have higher correlations with the U.S. News ranking than hiring-based measures (in-adjacency, hiring, and authority). The result is partly due to the nature of hiring in LIS: on the one hand, LIS hires faculty members from a variety of disciplines; on the other hand, LIS mainly places its graduates within this discipline. Thus,
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Fig. 5. Downward vs. upward placement among 44 LIS schools. Table 2 Distribution of download and upward placements among four tiers.
Downward Upward
Tier one
Tier two
Tier three
Tier four
154 (69.4%) 27 (38.6%)
65 (29.3%) 27 (38.6%)
1 (0.4%) 8 (11.4%)
2 (0.9%) 8 (12.4%)
placement data better reflect LIS schools’ performances within this discipline. For this reason, placement is seen as a more accurate indicator than hiring in deriving LIS ranking using LIS faculty hiring network. 4.2. Results of academic inequality Fig. 5 shows downward and upward placements among 44 LIS schools. The 44 LIS schools were positioned based on their ranks (i.e., from rank 1 (the leftmost school) to rank 44 (the rightmost school). In addition, the 44 LIS schools were equally divided into four tiers (i.e., rank 1 ∼ 11, rank 12 ∼ 22, rank 23 ∼ 33, and rank 34 ∼ 44) and are color-coded. The top half of the figure denotes downward placement (i.e., placements from the schools at left to the schools at right) while the bottom half denotes upward placement (i.e., directions of the arcs are from right to the left). The size of each node is proportional to its placement (i.e., outdegree), and each school is represented by two nodes to signify its downward and upward placements. The width of the arcs is proportional to the size of placements between two schools. Fig. 5 illustrates that the distribution of faculty placement is highly skewed toward the downward side (i.e., 68% downward placement vs. 22% upward placement vs. 10% self-placement). It also shows that LIS schools of tiers three and four are not as competitive as schools in tiers one and two in producing LIS faculty, while they hired faculty mostly from schools of tier one and two. Table 2 shows the distribution of downward and upward placements among four tiers. LIS schools of tier one and tier two secured the most downward (98.7%) and upward (76%) placements. The result is not surprising because 14 out of 44 LIS schools do not have doctoral programs, and among them, 13 schools are included in tiers three and four (see Appendix A). Thus, among the 22 schools in tier three and tier four, only nine schools have or had (e.g., Texas Woman’s University LIS school has stopped accepting new doctoral students since 2013) doctoral programs. There are also a few exceptions. For example, Simmons College in tier one placed only one (the average number in tier one is 14) downward placement while SUNY Albany (tier three) placed seven (among total eight in tier three) upward placements. Performance of individual schools is analyzed based on the notion of expected upward and downward placements (Appendix D). Based on the rank of a school, we calculated two probabilities (i.e., upward and downward) that a school can place its graduates to other 43 schools. For example, Drexel University ranks 10th and has nine schools for upward placement and 34 schools for downward placement. Therefore, the school’s expected ratios for upward and downward placements are 0.21 and 0.79, respectively. We can then multiply the ratios with the total upward and downward placements (i.e., eight) to obtain the expected upward and downward placements (i.e., two and six, respectively). By comparing the difference between the actual
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Fig. 6. Distributions of faculty placement among 44 LIS schools. Table 3 Four maximum cliques in the faculty hiring network. Clique 1 UIUC (1) UNC (2) Washington (3) Syracuse (4) Drexel (10) FSU (13)
Clique 2
Clique 3
Clique 4
UIUC (1) UNC (2) Syracuse (4) Michigan (5) Rutgers (6) Texas (7)
UIUC (1) UNC (2) Syracuse (4) Rutgers (6) Drexel (10) FSU (13)
UIUC (1) UNC (2) Rutgers (6) Drexel (10) FSU (13) Milwaukee (15)
and the expected upward placements, we can tell an individual school’s performance. Based on this statistic (as shown in the parentheses), schools such as Syracuse (4), UCLA (3), Michigan (2), Albany (2), Washington (1), Rutgers (1), Indiana (1), Maryland (1), Pittsburg (1), and Alabama (1) performed better than expected. On the other hand, schools such as FSU (-2), UNT (-2), Milwaukee (-1), Madison (-1), SC (-1), Hawaii (-1), and Arizona (-1) performed slightly worse than the expected. The inequality in faculty hiring is manifested by three facts: first, the size of downward placement is much greater than that of upward placement; second, among downward placements, those made by highly ranked LIS schools accounted for a greater portion; third, among upward placements, most placements were secured by highly ranked LIS schools. Taking a step further, the academic inequality in faculty placement is also shown in the analysis of the relationship between placements and the number of LIS schools that made the placements (Fig. 6). In the analysis we only included LIS schools with doctoral programs. It shows that 20% of 30 LIS schools produced nearly 60% of all LIS faculty. The Gini coefficient of the faculty hiring network is 0.53, which denotes a strong inequality in faculty placement. The value is lower than that of other disciplines reported in previous studies, in which, Gini coefficients of communication science, computer science, business, and history are 0.72, 0.69, 0.62, and 0.72, respectively (Barnett et al., 2010; Clauset et al., 2015). Inequality is also apparent in the number of faculty produced by each LIS school (Fig. 6B). Only about 20% of LIS schools have placed more than 15 faculty members while more than 45% of schools have placed less than five faculty members into other LIS schools based on the data we collected. It is possible that the actual number would be greater than five, given that out data does not include retired faculty. Nevertheless, the differences among LIS schools in faculty placement are apparent. Inequality is also found in faculty hiring in addition to faculty placement. Identified maximum cliques (Table 3) in the faculty hiring network shows inequality exists in faculty hiring. We identified four maximum cliques with the size of six. The numbers in parentheses denote its rank. All LIS schools in Table 3 are ranked 15 or higher. It is noticeable that lower ranked schools have limited access to a broad range of LIS schools in terms of faculty hiring. Comparisons of egocentric networks of LIS schools with different ranks also reveal academic inequality. Fig. 7 shows the egocentric networks of four LIS schools with different ranks (i.e., rank 1, rank 12, rank 23, and rank 34). The figure illustrates that the number of neighbors of each ego is decreasing along with dropping ranks. It is also clear that they differ in density. A core/periphery structure is identified in Fig. 7, in which their coreness scores (Borgatti & Everett, 2000) are 0.353, 0.194, 0.035, and 0.023, respectively. Thus, top schools are positioned at the core of the network (e.g., UIUC) while low ranked schools are located in the periphery (e.g., Greensboro). This signifies that the core/periphery structure in the faculty hiring network is closely related to academic inequality in LIS. Overall, academic inequality exists in both placement and hiring: 1) higher-ranked schools actively place their graduates to lower-ranked schools while tend to hire from schools with similar ranks; 2) lower-ranked schools rarely place their graduates to higher-ranked schools and tend to hire from schools similar to their rankings.
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Fig. 7. Egocentric networks of four LIS schools with different ranks (one from each tier).
5. Discussion and conclusion In this paper, we studied academic ranking and inequality in LIS by examining LIS faculty hiring networks. Forty-four LIS schools ranked by U.S. News were selected, and their hiring/placement relations were captured to build a LIS faculty hiring network. We evaluated four groups of network-based measures and their relationships with the U.S. News ranking that include adjacency, placement and hiring, distance-based measures, and hubs and authorities. Findings showed that, placement-based measures have higher correlations with the U.S. News ranking than hiring-based measures. The results demonstrated that LIS schools’ ability of placing graduates aligns more closely with their U.S. News rankings than their ability of hiring graduates. As a single measure, closeness (undirected) and the hub measure had the highest correlation with the U.S. News ranking (both had r = 0.78). Among integrated measures, adjacency (out + in) had the highest correlation (r = 0.83), followed by an integrated measure of placement and hiring (r = 0.82). In the study of LIS faculty placement inequality, we found that the percentage of downward placement (68%) is much higher than that of upward placement (22%). Meanwhile, results showed that 20% of the 30 LIS schools that have doctoral programs produced nearly 60% of all LIS faculty, with a Gini coefficient of 0.53. The value is lower than that of other disciplines reported in previous studies. However, the value still denotes a strong inequality in faculty placement. We also found four maximum cliques that comprised only highly ranked schools. A core/periphery structure was identified in the network, in which highly ranked schools positioned themselves in the core of the network while low ranked schools are in the periphery. The study has a few limitations. As previously mentioned, among the 44 LIS schools, only 30 schools have doctoral programs. Therefore, schools without doctoral programs were unrepresented in measures such as out-adjacency and placement. Given that out-degree-based measures (i.e., out-adjacency, placement, and hub) have higher correlations with the U.S. News ranking than in-degree-based measures (i.e., in-adjacency, hiring, and authority), the existence of doctoral programs plays an important role in LIS schools ranking. As described in our previous study (Zhu, Yan, & Song, 2016), 60% of LIS faculty members received doctoral degrees in LIS and this share has declined in favor of Computer Science graduates. In a way, this is a limitation of the current methods, in that it focused on LIS programs, even though we are moving to a point where the majority of faculty might be from outside the field—we need to reconceptualize what this field is—yet, this reconceptualization is beyond the scope of this study. Another limitation is that the study only explored faculty members who received doctoral degrees in American LIS schools and excluded international programs. However, this issue was mitigated given that 90% of faculty members received doctoral degrees in the U.S. (Zhu et al., 2016). Due to the above limitations, we had to study the problem with a partial dataset. For example, several schools such as University of Pittsburg, University of Arizona, Clarion University of Pennsylvania, Syracuse University, University of Texas at Austin, and University of Michigan have fewer than 40% of faculty members who graduated from LIS programs. This partial dataset may affect the analytical results made in the article. Another limitation is that the analysis in the study is static (i.e., current faculty) and does not show the evolution of
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LIS faculty hiring over time. Last, we tackled academic ranking and inequality only from the perspective of faculty hiring; however, academic careers are not the only career options for LIS graduates, and the results and conclusions may be different if we consider non-academic career options. The high correlations between network measures derived from the LIS faculty hiring network and the U.S. News ranking show the value of LIS faculty hiring network in understanding LIS academic ranking and can be used as an effective measure to rank LIS schools. While the current LIS ranking performed by U.S. News is a time-consuming process that involves much human involvement (e.g., surveys), the introduced measures can efficiently supplement the U.S. News ranking by enabling efficient and interpretable results. A concern out of these LIS rankings is whether they are actually associated with, and result in academic inequality. Unfortunately, we did observe academic inequality exists in the LIS community, from the aspect of faculty hiring and placement, through a LIS faculty hiring network. We believe that the results can be used to initiate candid conversation on ways to reduce academic inequality. In this regard, we expect future studies would contribute to the issue. Authors contributions Yongjun Zhu: Conceived and designed the analysis, Collected the data, Contributed data or analysis tools, Performed the analysis, Wrote the paper. Erjia Yan: Collected the data, Contributed data or analysis tools, Wrote the paper. Acknowledgments We thank Dr. Cassidy R. Sugimoto of Indiana University for her valuable feedback on an earlier version of this paper. This project was made possible in part by the Institute of Museum and Library Services (Grant Award Number: RE-07-15-006015), for the project titled “Building an entity-based research framework to enhance digital services on knowledge discovery and delivery”. Appendix A. The list of 44 LIS schools U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Institution
Abbreviation
Doctoral program
Placement
Hiring
University of Illinois at Urbana–Champaign University of North Carolina at Chapel Hill University of Washington Syracuse University University of Michigan Rutgers University University of Texas at Austin Indiana University Bloomington Simmons College Drexel University University of Maryland, College Park University of Pittsburgh Florida State University University of California, Los Angeles University of Wisconsin-Milwaukee University of Wisconsin-Madison University of Tennessee Kent State University University of Alabama University of South Carolina University of North Texas University of Kentucky University of North Carolina at Greensboro University of Oklahoma University of South Florida Wayne State University Catholic University of America Dominican University Louisiana State University University of Hawaii Pratt Institute SUNY Albany San Jose State University University of Arizona University of Denver University of Iowa University of Missouri Texas Woman’s University
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Kentucky Greensboro Oklahoma USF Wayne CUA Dominican LSU Hawaii Pratt Albany SJSU Arizona Denver Iowa Missouri TWU
Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N N Y N N N Y N Y N Y N Y N Y Y Y
33 31 7 26 17 34 13 14 2 9 8 30 20 15 4 11 3 1 3 2 14 0 0 1 0 0 0 0 0 1 0 7 0 4 0 0 7 4
10 15 9 10 6 7 6 6 16 13 8 6 12 4 12 3 10 11 9 10 14 8 6 8 7 4 6 6 6 4 4 4 9 6 4 3 6 9
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39 40 41 42 43 44
LIU Post SUNY Buffalo City University of New York North Carolina Central University Clarion University of Pennsylvania St. John’s University
LIU Buffalo CUNY NCCU Clarion SJU
Y N N N N N
3 0 0 0 0 0
5 3 8 7 2 3
Appendix B. Ranks of the 44 LIS schools in the employed measures U.S. News 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Institution
A(O)
A(I)
A
P
H
P+H
B(D)
B(U)
C(D)
C(U)
HU
AU
HU+AU
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Kentucky Greensboro Oklahoma USF Wayne CUA Dominican LSU Hawaii Pratt Albany SJSU Arizona Denver Iowa Missouri TWU LIU Buffalo CUNY NCCU Clarion SJU
4 2 16 4 6 1 11 9 22 13 14 3 6 6 18 11 20 26 22 22 10 30 30 26 30 30 30 30 30 26 30 14 30 22 30 30 17 18 20 26 30 30 30 30
10 1 15 15 27 15 23 27 2 5 23 36 5 36 5 36 10 2 15 5 4 10 15 15 10 27 23 27 15 27 36 27 5 27 27 36 27 10 23 36 36 15 44 36
3 1 14 4 8 2 12 9 14 9 14 5 5 9 14 13 21 18 24 21 7 27 30 27 27 36 34 36 30 34 40 18 24 30 36 40 23 18 24 36 40 30 44 40
2 3 15 5 7 1 11 9 24 13 14 4 6 8 18 12 21 26 21 24 9 30 30 26 30 30 30 30 30 26 30 15 30 18 30 30 15 18 21 26 30 30 30 30
8 2 12 8 23 20 23 23 1 4 16 23 5 34 5 40 8 7 12 8 3 16 23 16 20 34 23 23 23 34 34 34 12 23 34 40 23 12 33 40 16 20 44 40
2 1 14 4 8 3 11 10 13 9 14 4 6 11 14 17 18 21 21 21 7 28 33 26 31 38 33 33 33 37 38 24 26 25 38 42 18 18 28 38 28 31 44 42
12 1 10 5 11 4 13 17 21 20 16 7 8 6 19 18 14 23 24 15 3 29 29 9 29 29 29 29 29 28 29 2 29 25 29 29 22 26 27 29 29 29 29 29
14 2 32 8 11 1 10 4 21 12 19 6 7 5 17 9 16 18 31 15 3 23 33 30 24 34 36 43 29 35 44 13 25 27 37 38 20 22 28 40 42 26 41 39
12 4 10 3 2 1 7 9 28 8 14 6 12 5 20 17 22 26 21 27 15 30 30 19 30 30 30 30 30 28 30 11 30 23 30 30 24 25 18 16 30 30 30 30
14 1 19 3 4 2 10 6 24 11 19 6 6 6 19 11 19 11 31 17 4 25 15 34 18 36 29 41 28 31 44 15 26 39 37 35 31 19 26 37 42 29 42 40
1 3 13 5 7 2 12 9 21 11 14 4 6 8 22 15 18 27 19 29 10 32 32 24 32 32 31 32 32 28 40 16 30 23 32 40 17 20 26 25 40 32 40 40
9 7 12 5 28 19 18 21 1 6 23 17 3 40 2 39 11 10 13 14 4 25 22 16 30 42 26 27 34 38 29 33 20 32 41 35 36 15 31 37 8 24 43 44
1 2 11 3 15 5 14 12 8 9 17 6 4 21 10 26 16 18 19 22 7 30 25 23 36 42 31 32 38 39 33 27 24 35 41 40 28 20 34 37 13 29 43 44
1. A(O): Adjacency. out. 2. A(I): Adjacency.in. 3. A: Adjacency.out + in. 4. P: Placement. 5. H: Hiring. 6. P + H: Placement + Hiring. 7. B(D): Betweenness.directed. 8. B(U): Betweenness.undirected. 9. C(D): Closeness.directed. 10. C(U): Closeness.undirected. 11. HU: Hub. 12. AU: Authority. 13. HU + AU: Hub + Authority.
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Appendix C. Ranks of the 30 LIS schools measured by the ratio between placement/hiring and school size U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 28 30 32 34 36 37 38 39
Institution
Rank (school size)
Rank (placement/size)
Rank (hiring/size)
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Oklahoma Dominican Hawaii Albany Arizona Iowa Missouri TWU LIU
5 7 2 3 1 16 12 17 11 4 9 8 10 20 6 27 18 14 23 19 13 24 21 28 29 15 30 25 22 26
5 4 21 10 14 1 12 7 26 18 15 3 8 6 23 2 22 28 19 24 11 27 29 25 9 20 30 13 16 17
22 9 28 25 30 18 26 20 1 19 24 29 10 27 17 21 6 8 2 7 3 5 16 12 13 23 11 15 4 14
Appendix D. Individual school’s performance in upward and downward placements (30 schools with doctoral programs) U.S. News Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 28 30 32 34 36 37 38 39
Institution
UIUC UNC Washington Syracuse Michigan Rutgers Texas Indiana Simmons Drexel Maryland Pittsburgh FSU UCLA Milwaukee Madison Tennessee Kent Alabama SC UNT Oklahoma Dominican Hawaii Albany Arizona Iowa Missouri TWU LIU
Total w/o selfplacement 31 30 6 23 14 33 13 14 1 8 8 26 18 15 4 11 3 1 1 2 11 1 0 1 7 1 0 4 3 2
Expected upplacement 0 1 0 2 1 4 2 2 0 2 2 7 5 5 1 4 1 0 0 1 5 1 0 1 5 1 0 3 3 2
Expected downplacement 31 29 6 21 13 29 11 12 1 6 6 19 13 10 3 7 2 1 1 1 6 0 0 0 2 0 0 1 0 0
Actual upplacement 0 1 1 6 3 5 3 3 0 2 3 8 3 8 0 3 1 0 1 0 3 1 0 0 7 0 0 3 3 2
Actual downplacement 31 29 5 17 11 28 10 11 1 6 5 18 15 7 4 8 2 1 0 2 8 0 0 1 0 1 0 1 0 0
Difference in up-placement (actual-expected) 0 0 1 4 2 1 1 1 0 0 1 1 1-2 3 -1 -1 0 0 1 -1 -2 0 0 -1 2 -1 0 0 0 0
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