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Ordering the Global Field of Academic Science: Money, Mission, and Position

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Title: Ordering the Global Field of Academic Science: Money, Mission, and Position
Language: English
Authors: Cantwell, Brendan, Taylor, Barrett J., Johnson, Nathan M.
Source: Studies in Higher Education. 2020 45(1):18-33.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 16
Publication Date: 2020
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Global Approach, Research Universities, Scientific Research, Geographic Regions, Prediction, Reputation, Cross Cultural Studies, Science Education, Educational Resources, Competition, Comparative Education, Faculty Publishing, Institutional Mission, Scores, Institutional Evaluation
DOI: 10.1080/03075079.2018.1506916
ISSN: 0307-5079
Abstract: Researchers have identified the emergence of a global field of academic science. In order to understand the dynamics of this field, this study used latent profile and regression techniques to analyze data gathered for a sample of 114 research universities from around the world. Sociological theory informed the framing and conceptualization of the study. Results demonstrated that leading research universities emphasized different areas of science, that science emphasis was patterned by geographic region, and that region, resource levels, and science emphasis all predicted status in the field. Implications for theory and future research were discussed.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1238674
Database: ERIC
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  Value: <anid>AN0140974192;she01jan.20;2020Jan03.02:28;v2.2.500</anid> <title id="AN0140974192-1">Ordering the global field of academic science: money, mission, and position </title> <p>Researchers have identified the emergence of a global field of academic science. In order to understand the dynamics of this field, this study used latent profile and regression techniques to analyze data gathered for a sample of 114 research universities from around the world. Sociological theory informed the framing and conceptualization of the study. Results demonstrated that leading research universities emphasized different areas of science, that science emphasis was patterned by geographic region, and that region, resource levels, and science emphasis all predicted status in the field. Implications for theory and future research were discussed.</p> <p>Keywords: Rankings; comparative study; academic science; field theory; global competition</p> <p>Higher education is a global social enterprise (e.g. Marginson and van der Wende [<reflink idref="bib33" id="ref1">33</reflink>]; Altbach, Reisberg, and Rumbley [<reflink idref="bib2" id="ref2">2</reflink>]). Similarly, academic science is a global social field, pulling together people, ideas and resources across national borders (e.g. Marginson [<reflink idref="bib29" id="ref3">29</reflink>]; Drori and Krücken [<reflink idref="bib18" id="ref4">18</reflink>]; Drori et al. [<reflink idref="bib19" id="ref5">19</reflink>]). The 'global research university' (GRU) is a central institution in both arenas. World university rankings, which emphasize research, help to define the GRUs that are active in the global field of academic science (Hazelkorn [<reflink idref="bib24" id="ref6">24</reflink>]).</p> <p>Global rankings create a vertical hierarchy among GRUs, thereby stratifying institutions on a single axis. The imposition of a single hierarchy on a diverse group of institutions drawn from many different nations rests on the twin assumptions that these organizations are inherently comparable and fairly compared. Only when these assumptions go unquestioned – when one university is assumed to be comparable to another, regardless of national context – do global rankings make sense (Pusser and Marginson [<reflink idref="bib43" id="ref7">43</reflink>]; Ordorika and Lloyd [<reflink idref="bib42" id="ref8">42</reflink>]; Shahjahan and Morgan, [<reflink idref="bib48" id="ref9">48</reflink>]).</p> <p>Similar assumptions of comparability are sometimes repeated in scholarship about global science. Particularly when rooted in neo-institutional theory, these accounts tend to contrast science with other domains rather than consider differences among fields of science (e.g. Schofer and Meyer [<reflink idref="bib46" id="ref10">46</reflink>]; Drori et al. [<reflink idref="bib19" id="ref11">19</reflink>]). In other words, by imagining global science as horizontally undifferentiated, one form is assumed equivalent to another. The assumption of vertical comparability that undergirds the hierarchy of global rankings is paralleled in the assumption of horizontal comparability that ignores differences among fields of science.</p> <p>Assumptions of vertical and horizontal comparability are likely to be unwarranted. The idea of a single undifferentiated vertical hierarchy of universities is ill suited to describe a field that is laden with power asymmetries (Pusser and Marginson [<reflink idref="bib43" id="ref12">43</reflink>]). Just so, different GRUs may emphasize different academic domains and usually adopt these positions in response to policy incentives (Marginson and Rhoades [<reflink idref="bib32" id="ref13">32</reflink>]; Taylor, Cantwell, and Slaughter [<reflink idref="bib52" id="ref14">52</reflink>]; Karseth and Solbrekke [<reflink idref="bib25" id="ref15">25</reflink>]). Horizontal differentiation becomes linked to vertical stratification when policymakers and university managers give preference to one academic domain over others (Slaughter and Cantwell [<reflink idref="bib49" id="ref16">49</reflink>]). Accordingly, universities may be horizontally differentiated as well as vertically stratified, with some GRUs emphasizing the biomedical sciences, others focused on computer science, engineering, and 'techno-sciences,' and still others pursuing a suite of these activities.</p> <p>Hints of the geopolitical power embedded in global rankings emerged with the first publication of the Academic Rankings of World Universities (ARWU) in 2003 by researchers at Shanghai Jiao Tong University. When first devised, ARWU was intended to assess the standing of Chinese universities in global academic science. Governments beyond China promptly read the rankings as a report card on competitive position in the global research economy. European ministers, for example, are reported to have immediately understood the rankings as an indicator that Europe lagged behind other major economies (Hazelkorn [<reflink idref="bib22" id="ref17">22</reflink>], 3–4). The ripple cause by ARWU heightened attention given to subsequent ranking systems, including the Times Higher Education (THE), first published in collaboration with QS. Within a few years, rankings captured the attention of government officials, university leaders, and even the public. For individual universities, moving up in a preferred ranking may itself be a goal distinct from the geopolitical implications. In other words, rankings provide a shared set of cultural practices for university mangers who use them to benchmark institutional progress. Even if ranking positioning is not a specific strategic goal for university leaders, rankings have demonstrated the power to normalize a particular form of academic excellence (Pusser and Marginson [<reflink idref="bib43" id="ref18">43</reflink>]).</p> <p>The geopolitical and cultural normalizing influence of rankings imposes vertical differentiation, which in turn has generated substantial scholarly interest (e.g. Hazelkorn [<reflink idref="bib23" id="ref19">23</reflink>]). However, attention to vertical differentiation may result in overlooking potentially meaningful horizontal differences embedded within rankings. If global league tables subtly prefer one form of science to another, then rankings privilege some scientific activities over others. In other words, horizontal differences would be linked to vertical stratification, which in turn would call into even greater doubt the assumptions of comparability that undergird global rankings. Invisible preferences for some scientific emphases over others would mean that universities would be neither truly comparable nor fairly compared.</p> <p>We argue that the lack of attention to horizontal differentiation among global research universities has led to a partial understanding of vertical stratification, and so has obscured important power dynamics in the field of global science. While global ranking schemes purport to treat all scientific enterprises – and the resources that sustain them – equally, it is possible that different forms of academic science are rewarded differentially by global rankings. For example, rising universities in Asia have expanded production in chemistry and engineering sciences, whereas GRUs in the US and UK have tended to emphasize the biomedical sciences (Marginson [<reflink idref="bib31" id="ref20">31</reflink>]).</p> <hd id="AN0140974192-2">Study purpose and research questions</hd> <p>The purpose of this paper is to explore the relationship of horizontal differentiation (i.e. the various scientific emphases of GRUs) to vertical differentiation (i.e. position in global league tables). In order to conceptualize GRUs within the field of global science, we utilize theory from both Bourdieusian and neo-institutional sociological thought, and draw especially from Fligstein and McAdam's ([<reflink idref="bib20" id="ref21">20</reflink>]) <emph>A Theory of Fields</emph>, which combines these traditions into a framework. We assembled an international sample of 113 GRUs that appeared in the top 100 of various global league tables. Latent profile analysis (LPA) and regression allowed us to demonstrate that horizontal and vertical differentiation are closely related to one another because some scientific emphases yield higher ranking positions even net of total resources.</p> <p>The first aim of the study is to identify the distinct science profiles found among GRUs. We ask: What different scientific emphases are found among GRUs? LPA analyses of scientific outputs identified discrete scientific profiles for each sampled university, thereby indicating the different horizontal positions that universities could occupy. These profiles are not distributed randomly around the world. The global field of academic science is ordered, at least partly, by geography (Marginson and Rhoades [<reflink idref="bib32" id="ref22">32</reflink>]), and the rankings have prompted geopolitical competition from the very start. Hence, our second research question: How do scientific emphases vary geographically? Finally, policymakers and university leaders often understand global higher education as ordered vertically by world rankings (Hazelkorn, [<reflink idref="bib24" id="ref23">24</reflink>]). We argue that vertical position may not be independent from horizontal differences. Therefore, as a third research question, we ask: Is a particular scientific emphasis a source of advantage (or disadvantage) in global rankings?</p> <hd id="AN0140974192-3">GRUs in context</hd> <p>In all but the lowest income countries, tertiary participation levels have outpaced economic growth and skills demands by the labor market. These patterns signify the development of high participation systems of higher education, in which attending a college or university is an ordinary social expectation (Cantwell, Marginson, and Smolentseva [<reflink idref="bib9" id="ref24">9</reflink>]; Marginson [<reflink idref="bib31" id="ref25">31</reflink>]). Although higher education has assumed many forms – including vocational and proprietary schools – the science-oriented research university is globally the most prominent among them (Mohrman, Ma, and Baker [<reflink idref="bib37" id="ref26">37</reflink>]; Drori et al. [<reflink idref="bib19" id="ref27">19</reflink>]). Global output of science and engineering (S&E) articles approximately doubled from about 1.1 million in 2003 to around 2.2 million in 2013. Over that period the United States' share of publications declined from 27% to 19%, even though the number of US articles grew. This relative decline reflected even faster rates of growth in other countries, especially China (NRC [<reflink idref="bib40" id="ref28">40</reflink>], table 5-3).</p> <p>States have underwritten the global growth of science. Many governments believe research is central to economic competitiveness and craft policy that is explicitly intended to foster economic activity through investment in GRUs (Marginson [<reflink idref="bib30" id="ref29">30</reflink>]). GRUs emphasize research in science, technology, and engineering, and tend to follow Anglo-American logics of organization (Deem, Mok, and Lucas [<reflink idref="bib16" id="ref30">16</reflink>]; Mohrman, Ma, and Baker [<reflink idref="bib37" id="ref31">37</reflink>]). Although locally and nationally funded, these universities are 'global' because they are integrated with the global political economy (Kauppinen and Cantwell [<reflink idref="bib26" id="ref32">26</reflink>]).</p> <p>Further, universities compete for status and resources at local, national, and global levels (Marginson and Rhoades [<reflink idref="bib32" id="ref33">32</reflink>]). World university rankings have further spurred cross-border competition among universities seeking to maximize revenues and scientific outputs (Hazelkorn [<reflink idref="bib23" id="ref34">23</reflink>]; Oleksiyenko [<reflink idref="bib41" id="ref35">41</reflink>]). Although a focus on rankings position may weaken national systems overall (Cremonini et al. [<reflink idref="bib13" id="ref36">13</reflink>]), governments in many countries, especially in Europe and Asia, initiated 'excellence' programs to enhance global competitiveness, often defined vis-à-vis position in world rankings (Salmi [<reflink idref="bib44" id="ref37">44</reflink>]).</p> <hd id="AN0140974192-4">Conceptualizing a global field of academic science</hd> <p>The combination of worldwide expansion and diffusion of academic science and intensified cross-border competition situates GRUs in a global field. The concept of 'field' comes from sociology and is central to both neo-institutional and Bourdieusian traditions. The literature on the emerging global field of academic science has generally – though not always – drawn upon one or both of these traditions.</p> <p>In the neo-institutional tradition, a field is an environment shared by similar organizations (Meyer and Rowan [<reflink idref="bib36" id="ref38">36</reflink>]; DiMaggio and Powell [<reflink idref="bib17" id="ref39">17</reflink>]). Fields are composed of actors who face similar opportunities and constraints and are held together by the flow of ideas, habits, and personnel between organizations. Neo-institutional accounts posit the emergence of a 'world society' via macro social transformations – primarily, the spread of norms and cultural practices from the Global North – that form and stabilize fields (Schofer and Meyer [<reflink idref="bib46" id="ref40">46</reflink>]; Meyer [<reflink idref="bib35" id="ref41">35</reflink>]). In this account, the norms, values, and cognitive schema of the Global North are diffused worldwide, permitting the development of global fields – such as science – in which all actors share the same understandings and adhere to the same rules of practice.</p> <p>Bourdieusian sociology provides a useful companion lens through which to view the field of global academic science. Bourdieu conceptualizes fields as social-spatial domains in which actors, who occupy differing and unequal positions, deploy forms of capital to secure status (Bourdieu [<reflink idref="bib4" id="ref42">4</reflink>], [<reflink idref="bib5" id="ref43">5</reflink>], [<reflink idref="bib6" id="ref44">6</reflink>]). Fields, in other words, are highly stratified spaces. Actors structure hierarchies through field-specific conventions of practice and power relations. Although Bourdieu acknowledges individuals' agency, hierarchies prove durable, and field structure is ordinarily reproduced both within a single national system (e.g. Naidoo [<reflink idref="bib39" id="ref45">39</reflink>]) and across the global field of higher education (e.g. Marginson [<reflink idref="bib29" id="ref46">29</reflink>]).</p> <p>Together, neo-institutional and Bordieusian field concepts provide a rationale for understanding global rankings as a vertical hierarchy that reproduces inequality within a group of similar organizations. Rankings reproduce assumptions that are rooted in the norms and power relations of the Global North. However, neither theory attends closely to horizontal variation within a field. There are compelling reasons to think that horizontal differentiation occurs. Scientific output is patterned by institutional history, with some campuses focusing on engineering, others on the applied life sciences, and still others on a mixture of scientific activities. Emphases also vary by national context, with some countries emphasizing particular domains of science more heavily than others. For example, Chinese universities publish a significant share of highly cited articles in chemistry (Marginson, [<reflink idref="bib31" id="ref47">31</reflink>]).</p> <p>In order to account for these horizontal differences in scientific emphases, we draw upon Fligstein and McAdam's ([<reflink idref="bib20" id="ref48">20</reflink>]) <emph>A Theory of Fields</emph>, which builds upon the neo-institutional and Bourdieusian traditions. Fligstein and McAdam conceptualize fields as nested, interlocking, and inter-dependent arenas of social activity. The actors that populate 'strategic action fields' (SAFs) understand themselves to be members of the same field, at least when seeking particular goals. Perhaps the most common strategy for improving field position is to borrow ideas and resources from another field. Consider a university with a modest status in global league tables but with close connections to government agencies, industrial firms and other actors in the fields of physics, chemistry, and different physical sciences.</p> <p>Given that administrators and policymakers seem eager to improve their university's position in league tables (Hazelkorn [<reflink idref="bib23" id="ref49">23</reflink>]), decision-makers on such a campus might choose to partner closely with influential national actors to produce outputs that would yield improved position in the global academic science field, even if these outputs were not in local or national demand. Hence, a university may find its horizontal position changed due to the power relations implicit in global rankings. The power of rankings is vested in their ability to set standards, norm cultural practices, and prompt self-regulating actions. Policymakers and university administrators subsequently make decisions and deploy resources in accordance with these powerful ranking metrics (Pusser and Marginson [<reflink idref="bib43" id="ref50">43</reflink>]; Cantwell and Taylor [<reflink idref="bib10" id="ref51">10</reflink>]). To put it in terms consistent with Fligstein and McAdam ([<reflink idref="bib20" id="ref52">20</reflink>]), rankings help to set the rules and channel resources within SAFs. Power lies in the potential for rankings to indirectly compel universities, through the uptake of shared values and cultural practices, to prioritize specific areas of science. Policymakers and university administrators could conceivably respond to rankings by shifting resources into the areas of science that are most highly rewarded by the ranking systems, thereby shifting a university's horizontal position in an effort to maximize its vertical position. Thus, the decision to reallocate resources and readjust priorities for scientific inquiry might seem like a viable response to a ranking scheme that implicitly rewards specific kinds of scientific output.</p> <hd id="AN0140974192-5">Methods</hd> <p></p> <hd id="AN0140974192-6">Sample</hd> <p>There is no definitive list of GRUs so any investigation into this topic must define the sample. Our initial sample consisted of 119 GRUs that ranked in the top 100 of the 2014 CWTS Leiden rankings ([<reflink idref="bib15" id="ref53">15</reflink>]), ARWU rankings, or both. The Leiden rankings are a bibliometric ordering of universities by research output, and the ARWU rankings also heavily emphasize research. Because both rankings emphasize scientific output, they were appropriate for defining a sample within which to study the field of global academic science. Universities that met the ranking criteria but did not offer both undergraduate and graduate programs (e.g. Rockefeller University in the United States) were removed.</p> <p>This sample was drawn from multiple national systems. As a result, data were available inconsistently. We were able to identify areas of scientific emphasis for 114 (96%) of 119 total universities. Our analyzed sample therefore included the great majority of all possible observations. GRUs included in the sample were located in the United States (<emph>n</emph> = 54), United Kingdom (<reflink idref="bib9" id="ref54">9</reflink>), Netherlands (<reflink idref="bib7" id="ref55">7</reflink>), Australia (<reflink idref="bib6" id="ref56">6</reflink>), Canada (<reflink idref="bib5" id="ref57">5</reflink>), China (<reflink idref="bib5" id="ref58">5</reflink>), Germany (<reflink idref="bib5" id="ref59">5</reflink>), Switzerland (<reflink idref="bib5" id="ref60">5</reflink>), France (<reflink idref="bib3" id="ref61">3</reflink>), Japan (<reflink idref="bib3" id="ref62">3</reflink>), Belgium (<reflink idref="bib2" id="ref63">2</reflink>), Denmark (<reflink idref="bib2" id="ref64">2</reflink>), Singapore (<reflink idref="bib2" id="ref65">2</reflink>), Finland (<reflink idref="bib1" id="ref66">1</reflink>), Israel (<reflink idref="bib1" id="ref67">1</reflink>), Norway (<reflink idref="bib1" id="ref68">1</reflink>), South Korea (<reflink idref="bib1" id="ref69">1</reflink>), Sweden (<reflink idref="bib1" id="ref70">1</reflink>), and Taiwan (<reflink idref="bib1" id="ref71">1</reflink>). No GRUs from Africa or Latin America met the sampling criteria. A list of sampled universities, sorted by science emphasis and country, may be found in the appendix.</p> <hd id="AN0140974192-7">Data</hd> <p>Publication output data came from the CWTS Leiden Ranking, which provides standardized measures for article publications counted on a fractional basis. Counts are available for total article output by university, as well as article outputs in broad academic disciplines. The Leiden rankings also identify the share of papers that are among the top 10% cited articles in their respective disciplines. The 2014 counts used in this study included articles published over the period 2009–2012. When a university produced fewer than 100 articles in a particular discipline, the Leiden Rankings counted this as not being active in that discipline (CWTS Leiden Rankings [<reflink idref="bib15" id="ref72">15</reflink>]). Other raking data used in this study were ARWU 'raw scores' from the 2014 edition (ARWU [<reflink idref="bib1" id="ref73">1</reflink>]) and THE scores from the same year (THE 2014). These data collection patterns mimicked the underlying process of interest, in which scientific activity occurred first (2009–2012) and was ranked subsequently (2014).</p> <p>Organizational data were gathered for each GRU in the sample. Because data were collected from numerous countries and institutions, variation in reporting was inevitable. However, data were carefully evaluated and selected to provide as much comparability as possible. Because the United States dominated the sample, US sources provided the starting point and baseline for data collection. The 2012–2013 IPEDS survey, administered by the US Department of Education, was used to gather most data for US GRUs. Operating budget data were collected from individual institutional websites and financial statements representing the 2012 fiscal year. To parallel the information collected on US universities, the search for data outside the US looked to sources from the 2012–2013 academic year or its nearest equivalent, and that reported information across multiple universities consistently. For each country with institutions represented in the sample, we first sought to locate sources that collected and reported data similar to IPEDS. These types of resources were available in Australia (The Department of Education and Training), Canada (The Association of Universities and Colleges of Canada), and the United Kingdom (Higher Education Statistics Agency). However, as with the US, financial data for these three countries also were gathered from university websites and financial statements.</p> <p>Data collection from institutions outside these English-speaking countries was dependent on information made available through university websites. Of course, data were not always available in English, so translators or translation software were used. Country experts were also contacted to help interpret these data and match them to the categories used by the English-speaking countries. In the case of financial data, non-US currencies were converted to US dollars based on the 2013 yearly average currency exchange rates provided by the Internal Revenue Service.</p> <hd id="AN0140974192-8">Latent profile analysis</hd> <p>Our first research question conceptualized a university's science emphasis as fitting within a series of non-overlapping, non-hierarchical categories. A university's science profile may be intuited from theory but cannot be measured directly. As such, it is a latent construct (Croon [<reflink idref="bib14" id="ref74">14</reflink>]).</p> <p>When a sample is assumed to contain multiple subsamples – for example, different science profiles – latent variables may be identified through an analysis of observed indicators with which they are correlated (McCutcheon [<reflink idref="bib34" id="ref75">34</reflink>]). For example, the number of peer-reviewed papers published in a particular area, such as the life sciences, may indicate a university's science emphasis. We used MPlus version 6.0 software to conduct LPA (Vermunt and Magidson [<reflink idref="bib55" id="ref76">55</reflink>]; Muthén and Muthén [<reflink idref="bib38" id="ref77">38</reflink>]). To reduce the chance of identifying local maxima rather than discrete classes (Geiser [<reflink idref="bib21" id="ref78">21</reflink>]), we conducted our analysis with 1,000 random starts and 200 optimizations.</p> <p>We used three broad groups of variables to identify scientific emphases. First, in order to measure the total volume of scientific output, we included the total number of peer-reviewed papers per academic staff member. Second, we wanted to measure publications' status, and so included the percentage of papers in the top 10% of total citations. Third, we were interested in the possibility that a university might emphasize certain scientific areas rather than others – in other words, that one group of universities might be horizontally differentiated from another. We therefore included the percentage of papers published in various scientific areas: the life sciences; computer sciences, math and engineering (CSME); medicine; the natural and environmental sciences; and the cognitive and social sciences. The resulting analysis richly reflected the scale, impact, and subject areas of a particular university's scientific emphasis.</p> <hd id="AN0140974192-9">The geography of academic science</hd> <p>After identifying GRUs' scientific emphases, we sought to understand the global geography of academic science. Accordingly, we constructed a variable that corresponded to the United Nations' ([<reflink idref="bib54" id="ref79">54</reflink>]) geographical sub-regions. This measure allowed us to consider the geographic distribution of scientific emphases.</p> <hd id="AN0140974192-10">Regression analysis</hd> <p>Finally, we were interested in whether a particular suite of scientific activities netted greater returns in the global field, net of other factors, than did others. In other words, we explored whether horizontal differentiation was related to vertical stratification. We used ordinary least-squares (OLS) regression (Cameron and Trivedi [<reflink idref="bib8" id="ref80">8</reflink>]) to predict a university's raw score on two prominent global rankings, ARWU and THE. Our independent variables of interest were the scientific emphases discovered through LPA. We specified this relationship net of the following control characteristics:</p> <p></p> <ulist> <item> League tables such as ARWU often legitimate or even reward financial stratification between universities (Cantwell and Taylor [<reflink idref="bib10" id="ref81">10</reflink>]). Operating expenditures per full-time academic staff member tested the possibility that well-resourced universities received higher ARWU scores than did their peers.</item> <p></p> <item> Universities are multi-purpose organizations that can reallocate resources from one activity (e.g. teaching) to another (e.g. research) (Leslie et al. [<reflink idref="bib27" id="ref82">27</reflink>]).[<reflink idref="bib1" id="ref83">1</reflink>] It was, therefore, important to account for the possibility that large enrollments facilitated greater institutional support for research. We included full-time equivalent (FTE) enrollment to test this possibility and included the square of this term to test for (dis)economies of scale.</item> <p></p> <item> The growth of administrative steering cores may represent a plausible response to growing uncertainty and heightened competition (Clark [<reflink idref="bib12" id="ref84">12</reflink>]), but it also may route resources away from the academic enterprise (Tuchman [<reflink idref="bib53" id="ref85">53</reflink>]). The percentage of total staff that is academic tested the possibility that a university's emphasis on academic production was positively associated with ARWU score.</item> <p></p> <item> North American universities hold a large share of the top slots in global league tables (Marginson [<reflink idref="bib31" id="ref86">31</reflink>]). Including a series of variables indicating UN geographic sub-region tested the possibility that universities from some areas fared especially well in rankings.</item> <p></p> <item> In addition to these independent variables, we also interacted our independent variables of interest – scientific emphases – with operating expenditures per full-time academic staff member. These interactions tested the possibility that money netted different returns based on scientific emphasis.</item> </ulist> <p>Descriptive statistics are presented in Tables 1 and 2.</p> <p>Table 1. Description of selected variables (not included in LCA)<emph>.</emph></p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variables</td><td>Mean (standard deviation)</td></tr></thead><tbody><tr><td>ARWU score</td><td char="(">33.727 (13.727)</td></tr><tr><td>THE score</td><td char="(">60.204 (34.192)</td></tr><tr><td>Operating expenditures per member of academic staff (in thousands)</td><td char="(">649.303 (432.949)</td></tr><tr><td>Full-time equivalency enrollment</td><td char="(">29,241.0 (13,460.9)</td></tr><tr><td>Percentage of staff that is academic</td><td char="(">39.8% (14.3)</td></tr><tr><td>UN region: Northern America</td><td>51.3%</td></tr><tr><td>UN region: Eastern Asia</td><td>8.8%</td></tr><tr><td>UN region: South-Eastern Asia</td><td>1.8%</td></tr><tr><td>UN region: Western Asia</td><td>0.9%</td></tr><tr><td>UN region: Northern Europe</td><td>12.4%</td></tr><tr><td>UN region: Western Europe</td><td>18.6%</td></tr><tr><td>Australia and New Zealand</td><td>6.2%</td></tr><tr><td>Total observations</td><td>113</td></tr></tbody></table> </ephtml> </p> <p>Table 2. Means of members of four latent classes of WCUs (standard deviations in parentheses).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Variables</td><td>Sample average</td><td>Biomedical universities</td><td>Techno-science universities</td><td>Traditional science universities</td><td>Harvard University</td></tr></thead><tbody><tr><td>Total papers per academic staff member</td><td>2.918 (1.782)</td><td>2.574** (1.038)</td><td>3.604 (2.391)</td><td>2.981 (1.440)</td><td>14.126</td></tr><tr><td>Percentage of papers in the top 10% of highly cited papers</td><td>14.3% (3.5)</td><td>14.4% (2.6)</td><td>17.9%** (3.9)</td><td>11.4%** (2.8)</td><td>23.0%</td></tr><tr><td>Percentage of papers in the social and cognitive sciences</td><td>20.0% (7.8)</td><td>23.3%** (5.7)</td><td>14.9%** (6.4)</td><td>13.9%** (8.6)</td><td>23.0%</td></tr><tr><td>Percentage of papers in computer science, mathematics, and engineering (CSME)</td><td>9.7% (6.6)</td><td>6.5%** (3.5)</td><td>18.0%** (7.5)</td><td>13.7%** (5.4)</td><td>1.8%</td></tr><tr><td>Percentage of papers in the medical sciences</td><td>23.6% (12.1)</td><td>30.8%** (7.4)</td><td>4.4%** (2.3)</td><td>15.8%** (6.3)</td><td>39.1%</td></tr><tr><td>Percentage of papers in the life sciences</td><td>20.1% (4.7)</td><td>21.2%* (4.4)</td><td>14.7%** (5.1)</td><td>20.3% (2.5)</td><td>23.6%</td></tr><tr><td>Percentage of papers in the natural and environmental sciences</td><td>26.4% (12.9)</td><td>18.1%** (5.6)</td><td>47.2%** (7.6)</td><td>36.4%** (6.3)</td><td>12.9%</td></tr><tr><td>Total observations</td><td>114</td><td>71</td><td>17</td><td>25</td><td>1</td></tr></tbody></table> </ephtml> </p> <p>Results of 'two-tailed' t-tests indicating whether class differs from sample mean depicted as **<emph>p</emph> <.01, *<emph>p</emph> <.05.</p> <p>OLS regression is a robust analytic technique and tends to perform better than maximum likelihood-based estimators in small datasets such as ours (Long [<reflink idref="bib28" id="ref87">28</reflink>]). Nonetheless, our dataset violates the assumptions of OLS because data are grouped in meaningful ways. Universities remain situated in national contexts even as they compete globally (Marginson and Rhoades [<reflink idref="bib32" id="ref88">32</reflink>]). Accordingly, we clustered standard errors by country to allow for patterned similarities within a national system.</p> <hd id="AN0140974192-11">Results</hd> <p></p> <hd id="AN0140974192-12">Horizontal differentiation: latent profiles of GRUs</hd> <p></p> <hd id="AN0140974192-13">Model selection</hd> <p>LPA identified four groups of universities. This analysis addressed our first research question, which asked if GRUs take varying positions in the field. A bootstrap likelihood ratio (LR) difference test, which is a suitable means of determining relative model fit (Geiser [<reflink idref="bib21" id="ref89">21</reflink>]), indicated (<emph>p</emph> <.001) that our four-category model improved significantly upon a three-class model. In selecting a model, however, fit precision was balanced against parsimony and usefulness for theory and application. As a result, although a five-class model proved a moderately better match by the LR difference test, we present a four-category model because the five-category specification yielded little new information. The Bayesian Information Criterion (BIC) proved nominally similar in the four-category (4,951.11) and five-category models (4,933.21), suggesting that the five-category model improved little on the four-category analysis.</p> <hd id="AN0140974192-14">Model description</hd> <p>Table 2 presents descriptive statistics of the variables used to identify latent profiles. Measures for the individual categories were compared to the sample as a whole using one-tailed t-tests. For all sampled GRUs, publication activity centered upon the social and cognitive, medical, natural/environmental, and life sciences. Each of these broad areas contributed roughly one-fifth of total peer-reviewed publications, although medical science accounted for the largest share at almost one-quarter. By contrast, publication activity in CSME was modest at less than 10% of the total.</p> <p>The largest profile included more than 60% of sampled GRUs. We refer to this group as Biomedical universities because these GRUs emphasized medical science relative to all other fields. More than 30% of these universities' publications focused on medical science, with an additional 21% predominantly addressing the life sciences. Aggregate paper production was significantly lower than their peers. However, these papers were typical in their likelihood of being highly cited.</p> <p>We refer to members of the second group of GRUs as Techno-Science universities. The Techno-Science group included approximately 20% of sampled universities. Relative to their peers, these universities emphasized publication in CSME and the natural/environmental sciences. By contrast, members of the group de-emphasized the cognitive and social, life, and medical sciences. These universities experienced some success with the papers that their academic staff did publish, however, as their work was significantly more likely than the sample average to be highly-cited.</p> <p>We refer to members of the third group of GRUs as Traditional Science universities. Like techno-science universities, traditional science universities – approximately 12% of the sample – published a higher share of their papers in CSME and a lower share in the medical sciences than did other GRUs. Unlike Techno-Science universities, Traditional Science universities did not produce a large volume of high-citation work. The share of papers in the top 10% was the lowest of any group and fell significantly below the sample average.</p> <p>The fourth group included a single case, Harvard University. Because this class consisted of one organization, statistical tests could not be undertaken to determine its relationship to the sample. However, no statistical tests are necessary to demonstrate the sui generis character of this university. Harvard topped the ARWU rankings for every year through 2014 (although its position in THE has proven more volatile). Harvard published a far higher volume of papers per member of its academic staff than did other GRUs. These papers were much more likely to be highly cited than were others and were heavily concentrated in the medical, cognitive and social, and life sciences. CMSE scholarship was de-emphasized, constituting less than 2% of Harvard's scientific output.</p> <hd id="AN0140974192-15">The geography of horizontal differentiation</hd> <p>Table 3 reports the distribution of GRUs by category and UN geographical sub-region. More than half of our sample consisted of North American universities. Although all scientific emphases were present, almost two-thirds of North American GRUs fell into the Biomedical category. Techno-Science universities were secondary but substantial in number, comprising more than one-fifth of all North American GRUs. This meant that North America was home to more than 70% of sampled techno-science universities, though GRUs from Europe and Asia also belonged to this category. Western Europe, Northern Europe, and Australia and New Zealand together produced substantial numbers of GRUs, accounting for more than 35% of our sample. The majority of these universities (78.6%, or 33 of 42) fit the Biomedical profile.</p> <p>Table 3. WCUs' scientific emphases, disaggregated by UN geographical sub-region.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>UN sub-region</td><td>Total</td><td>Biomedical university</td><td>Techno-science university</td><td>Traditional science university</td><td>Harvard</td></tr></thead><tbody><tr><td>Northern America</td><td char="(">58 (50.9%)</td><td char="(">38 (65.5%)</td><td char="(">12 (20.7%)</td><td char="(">7 (12.1%)</td><td char="(">1 (1.7%)</td></tr><tr><td>Western Europe</td><td char="(">22 (19.3%)</td><td char="(">16 (72.7%)</td><td char="(">3 (13.6%)</td><td char="(">3 (13.6%)</td><td>0</td></tr><tr><td>Northern Europe</td><td char="(">14 (12.3%)</td><td char="(">11 (78.6%)</td><td>0</td><td char="(">3 (21.4%)</td><td>0</td></tr><tr><td>Eastern Asia</td><td char="(">10 (8.8%)</td><td>0</td><td char="(">1 (10.0%)</td><td char="(">9 (90.0%)</td><td>0</td></tr><tr><td>Australia and New Zealand</td><td char="(">7 (6.1%)</td><td char="(">6 (85.7%)</td><td>0</td><td char="(">1 (14.3%)</td><td>0</td></tr><tr><td>South-Eastern Asia</td><td char="(">2 (1.8%)</td><td>0</td><td char="(">1 (50.0%)</td><td char="(">1 (50.0%)</td><td>0</td></tr><tr><td>Western Asia</td><td char="(">1 (0.9%)</td><td>0</td><td>0</td><td char="(">1 (100.0%)</td><td>0</td></tr><tr><td>TOTAL</td><td>114</td><td>71</td><td>17</td><td>25</td><td>1</td></tr></tbody></table> </ephtml> </p> <p>Only in Eastern and South-Eastern Asia was something other than the Biomedical university dominant. Those regions included 12 sampled GRUs, 10 of which fit the Traditional Science profile, and 2 of which belonged to the Techno-Science category. No Asian universities belonged to the Biomedical group even though this was the most common profile overall. Of note, all Japanese and four of five Chinese GRUs in the sample classified as Traditional Science. The one GRU from Western Asia included also fit the Traditional Science profile. The great share (83.3%) of Asian GRUs in the Traditional Science group suggested that this category of horizontal differentiation was to some extent a proxy for geography.</p> <hd id="AN0140974192-16">Relating horizontal differentiation to vertical stratification</hd> <p>Our third research question examined the relationship between horizontal differentiation (e.g. scientific emphases discovered by LPA) and vertical stratification as measured by world rankings. Regression results appear in Table 4. The two columns of results represented two different global league tables, ARWU and THE. We determined the significance of interaction terms not by p-values, but by a test of joint significance that accounted for all three terms in the interaction (Brambor, Roberts, and Golder [<reflink idref="bib7" id="ref90">7</reflink>]). Results of these tests, presented alongside traditional regression output, indicated that the 'signs and significance' of the two models were similar. Biomedical universities receive a smaller intercept adjustment in the THE model than the ARWU one, but the differential return to expenditures (relative to the referent category of traditional science universities) was present in both analyses. In short, the two regression analyses were generally compatible.</p> <p>Table 4. OLS regression results.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>VARIABLES</td><td>(1)</td><td>(2)</td></tr><tr><td>ARWU score</td><td>THE score</td></tr><tr><td>Results</td><td>Jointly significant?</td><td>Results</td><td>Jointly significant?</td></tr></thead><tbody><tr><td>Operating expenditures per member of academic staff</td><td>0.00498 (0.00430)</td><td>NA</td><td>0.00645 (0.00628)</td><td>NA</td></tr><tr><td>Harvard</td><td>65.65** (4.978)</td><td>NA</td><td>26.67** (5.965)</td><td>NA</td></tr><tr><td>Biomedical university</td><td>−5.053 (4.085)</td><td>Yes (p ≈ 0.000)</td><td>0.674 (5.087)</td><td>Yes (p ≈ 0.004)</td></tr><tr><td>Interaction of biomedical university X operating expenditures per member of academic staff</td><td>0.00836* (0.00396)</td><td>0.00362 (0.00521)</td></tr><tr><td>Techno-science university</td><td>−5.349 (6.732)</td><td>Yes (p ≈ 0.008)</td><td>−1.456 (7.762)</td><td>Yes (p ≈ 0.000)</td></tr><tr><td>Interaction of techno-science university X operating expenditures per member of academic staff</td><td>0.0102* (0.00477)</td><td>0.00793 (0.00715)</td></tr><tr><td>Full-time equivalency enrollment</td><td>−0.000199 (0.000214)</td><td>No (p ≈ 0.379)</td><td>−0.000721* (0.000279)</td><td>No (p ≈ 0.056)</td></tr><tr><td>Square of full-time equivalency enrollment</td><td>1.94e-09 (2.78e-09)</td><td>8.78e-09* (3.58e-09)</td></tr><tr><td>Percentage of full-time staff that are academic</td><td>0.392* (0.161)</td><td>NA</td><td>0.195 (0.136)</td><td>NA</td></tr><tr><td>Geographic region: Eastern Asia</td><td>−11.45* (4.001)</td><td>NA</td><td>−7.464 (4.463)</td><td>NA</td></tr><tr><td>Geographic region: South-Eastern Asia</td><td>−19.88** (3.656)</td><td>NA</td><td>2.445 (2.448)</td><td>NA</td></tr><tr><td>Geographic region: Western Asia</td><td>−10.71** (3.421)</td><td>NA</td><td>−23.92** (3.851)</td><td>NA</td></tr><tr><td>Geographic region: Northern Europe</td><td>−1.144 (3.493)</td><td>NA</td><td>−0.0346 (6.117)</td><td>NA</td></tr><tr><td>Geographic region: Western Europe</td><td>−13.40** (3.605)</td><td>NA</td><td>−10.15** (2.764)</td><td>NA</td></tr><tr><td>Geographic region: Australia and New Zealand</td><td>−13.41** (2.189)</td><td>NA</td><td>−7.470** (2.359)</td><td>NA</td></tr><tr><td>Constant</td><td>22.39 (11.34)</td><td /><td>63.63** (11.98)</td><td /></tr><tr><td>Observations</td><td>113</td><td /><td>114</td><td /></tr><tr><td>R-squared</td><td>0.474</td><td /><td>0.357</td><td /></tr></tbody></table> </ephtml> </p> <p>Standard errors (clustered by country) in parentheses **<emph>p</emph> <.01. *<emph>p</emph> <.05.</p> <p>Results indicated that horizontal differentiation proved a significant predictor of ranking. An omnibus F test (<reflink idref="bib3" id="ref91">3</reflink>, 18) revealed that, relative to the referent category of the Traditional Science group, both the Biomedical university (<emph>p</emph> <.01) and the Techno-Science university (<emph>p</emph> <.001) categories interacted significantly with spending per member of academic staff. Universities in these classes started at different levels and netted differential returns to expenditures than did natural science universities. With other variables held constant and operating spending set at the sample mean, a Biomedical university could expect an upward adjustment of around 3.2 points, or 9.4% of the average ARWU score, relative to a Natural Science university. The THE analysis found a similar relationship of 12.0%. A Techno-Science university under the same conditions would receive a somewhat greater return (4.5 points or 13.4% for ARWU, 7.9 points 13.1% for THE).</p> <p>Because these relationships are complex, we present them graphically as well as numerically. Figure 1 compared the rate of return to ARWU score on expenditures at Biomedical, Techno-Science, and Traditional Science universities. As this figure indicated, a Biomedical or Techno-Science university could expect to net a higher ARWU score than would a Traditional Science university even if the two organizations expended equal amounts of money. The difference between Traditional Science universities and members of the other two groups was just under 2.0 points of ARWU score at the lowest level of expenditure.</p> <p>Graph: Figure 1. Predicted ARWU score by university profile as operating expenditures per academic staff member vary.</p> <p>What is more, the ranking advantages enjoyed by Biomedical and Techno-Science universities grew as expenditures increased. In Figure 1, this was indicated by the relatively steep slope of the Biomedical and Techno-Science lines. Substantively, this meant that the gap between favored and disfavored horizontal profiles widened as a university's financial resources grew. At the highest level of expenditure, a university in the Biomedical group netted about 6.0 points of ARWU score – more than one-sixth of the average value of this variable (see Table 1) – than would a Traditional Science university with identical expenditures. Additionally, as expenditures increased, Techno-Science universities opened a gap between their expected scores and those of Biomedical universities. Despite starting with similar predicted ratings, these two groups diverged by more than 1.0 point of ARWU score when expenditures per academic staff member reached $800,000. A Techno-Science university could expect to earn a higher ARWU score than would a Traditional Science university at any expenditure level and was predicted to fare better than a Biomedical university as expenditures exceeded the sample mean. In short, horizontal differentiation – scientific profile – was meaningfully associated with vertical stratification, and this association intensified as spending grew.</p> <p>Several other results merit brief mention. For example, the percentage of staff that were academic proved a positive predictor of ARWU score but not of THE rating. Further, many UN geographical sub-regions proved to be significantly negative predictors of ARWU score. Geographic patterns were less pronounced in the THE analysis but proved notable. These relationships refined our descriptive analysis undertaken in response to question two, implying that – even net of the geographic patterning of scientific emphasis – GRUs from outside the Global North faced a 'prestige penalty' in the rankings.</p> <hd id="AN0140974192-17">Discussion</hd> <p>In this study, we sought to better understand the relationship between horizontal differentiation and vertical stratification in the global field of academic science. LPA identified four distinct scientific emphases that corresponded to horizontal positions of GRUs: Biomedical universities, Techno-Science universities, Traditional Science universities, and Harvard University. These groupings were geographically patterned. Most North American, European, and Australian GRUs belonged to the Biomedical or, less frequently, Techno-Science groups. GRUs in Asia, by contrast, most commonly belonged to the Traditional Science group. Geography then proved an important predictor positions in the vertical hierarchies of league tables. Ranking scores were predicted by geography, with GRUs from North America and Northern Europe enjoying an automatic advantage even net of resources and scientific emphases.</p> <p>Of greater interest given our theoretical focus, horizontal differentiation – a GRU's scientific emphasis – also proved an important predictor of vertical position. What is more, the advantage associated with being a Biomedical university (relative to a Traditional Science university) was compounded as spending grew. This interaction indicated that, while spending large sums of money could lead to status, the return to spending was uneven. ARWU and THE appeared to reward spending only when it was allocated in specific ways. All else equal, funds spent in the pursuit of some activities – such as Biomedical and Techno-Science – led to a better position within the field than did an equivalent amount of money invested in Traditional Science fields. Universities that adopted Biomedical, and especially Techno-Science, emphases saw greater status returns on their resource expenditures than did universities with a Traditional Science emphasis. The interaction of money and mission appeared to be a significant predictor of ranking positions.</p> <p>This interaction also seems to benefit GRUs from North America, Europe, and Australia and New Zealand. At the national level, scientific emphases likely reflect cultural preferences, history of the type of science funded by governmental agencies, and long-standing and emerging science policy priorities. Among institutions, scientific emphases reflect the university mission, decisions to establish and develop graduate programs in particular fields, and the accumulation of decisions related to hiring researchers with specific expertise. When universities from these regions combine their resources and scientific practices with the automatic bias toward certain geographic regions, their advantages in global rankings are formidable.</p> <p>The status of Harvard, a university that was in a class of its own in our analysis, is also worth consideration. This finding may suggest that a good measure of cultural and resource advantage is tied up in a tiny group of super-incumbent GRUs, of which Harvard is the peak. Other ultra-elite universities, such as Oxford and Stanford, did not defy grouping in our analysis, but often do so in different domains such as staffing and research capacity (Cantwell and Taylor [<reflink idref="bib11" id="ref92">11</reflink>]; Taylor [<reflink idref="bib50" id="ref93">50</reflink>]). Examining the influence of Harvard and other ultra-elites may reveal field norms and expectations, as is commonly assumed. But research also could explore the possibility that super-elite universities may defy field rules – for example, as with Harvard, by deemphasizing technology – without paying a penalty in status.</p> <hd id="AN0140974192-18">Why does ranking position matter?</hd> <p>We argue that this study establishes a relationship between horizontal differentiation (the type of science a university does) with vertical order (the status hierarchy) in positioning GRUs relative to each other. Scores in ARWU and THE ranking systems were used to measure vertical position, which we took as a broad measure of status. Why care so much about an arbitrary position in a constructed competition?</p> <p>Ranking positions matter at least partially because they are associated with the flow of resources. Governments are attuned to rankings and have established 'excellence' and similar schemes to bolster the standing of GRUs (Hazelkorn [<reflink idref="bib23" id="ref94">23</reflink>], Salmi [<reflink idref="bib44" id="ref95">44</reflink>]). Universities that perform well on the rankings may enjoy additional resources from policymakers. In national systems that charge tuition fees, universities may also benefit from demand by students who are attracted by high rankings. There is some evidence to suggest that higher ranking positions result in increased revenue from fee paying students (e.g. Bastedo and Bowman [<reflink idref="bib3" id="ref96">3</reflink>]).</p> <p>Beyond direct resource considerations, the relationship between academic science type and world university ranking positions may be cultural (Sauder and Espeland [<reflink idref="bib45" id="ref97">45</reflink>]). Institutional inequality is on the rise both globally and within particular national systems (Marginson [<reflink idref="bib31" id="ref98">31</reflink>]; Slaughter and Cantwell [<reflink idref="bib49" id="ref99">49</reflink>]). Indeed, in large national systems such as the US, competitive dynamics akin to those that we document here can create a 'system of unequal higher education' in which an ever-greater share of resources and status are concentrated at an ever-smaller share of universities (Taylor and Cantwell [<reflink idref="bib51" id="ref100">51</reflink>]). There are reasons why such resource concentration may be efficient or effective, but at least as many reasons why it might be unjust. By blessing rising institutional inequality with the imprimatur of status, global rankings obscure these kinds of questions in policy discussions.</p> <p>Similar patterns may play out along the horizontal axis. Assuming rankings do hold the power to establish and diffuse cultural preferences among university administrators and policymakers, outsized returns in the rankings from universities in the Techno-Science and Biomedical profiles could result in widespread preferences for these fields of science over other types of scientific research. If such a preference were broadly adopted, the variety of knowledge produced by the global science system could be reduced. What is more, universities such as those in Japan that produce Traditional Science outputs could see eroded stats in the global field. Long-term results might include fewer international students and scholars interested in visiting Traditional Science universities, reduced research collaboration with these universities, declining investment in necessary fields (e.g. physical chemistry), and perhaps even the eventual isolation of Traditional Science universities from the global field.</p> <hd id="AN0140974192-19">Limitations and future research</hd> <p>Our analysis is limited in several ways. Some limitations are inherent to work of this kind. Theory and the literature informed our sample specification, but any sample is partially arbitrary. Our sample did not include GRUs from Africa or Latin America because these institutions did not appear in the ARWU or Leiden top 100. Future research may seek to analyze larger and differently defined samples. As noted, an additional challenge to assembling large samples is the availability and comparability of data. Higher education systems and data reporting standards are heterogeneous, making it likely that our measures, while broadly similar, are not precisely the same for all observations. Finally, there are some questions raised by this study – such as the relationship between cultural advantage and scientific emphasis – that can only be answered fully through theoretical analysis and deep case study.</p> <p>Above all, we call for more organizational research that is comparative and international in scope. Global processes partly shape the dynamics of educational organizations. However, most organizational research conforms to what Shahjahan and Kezar ([<reflink idref="bib47" id="ref101">47</reflink>]) describe as methodological nationalism in that it does not engage with the global dimensions of higher education and often assumes national conditions to be universal. Studies that resist these narrow assumptions can collectively illuminate the global aspects of contemporary higher education. Our results suggest that these global dimensions are fraught with power dynamics. While vertical stratification is well known and often criticized, our analysis suggests that the relationship between horizontal differentiation and vertical position also deserves further attention.</p> <hd id="AN0140974192-20">Disclosure Statement</hd> <p>No potential conflict of interest was reported by the authors.</p> <hd id="AN0140974192-21">Appendix table. List of sampled universities by latent class.</hd> <p></p> <p> <ephtml> <table><tbody><tr><td>Biomedical universities</td><td /><td /><td /></tr><tr><td>Monash University</td><td>Australia</td><td>University of Groningen</td><td>Netherlands</td><td>Stanford University</td><td>USA</td></tr><tr><td>The University of Melbourne</td><td>Australia</td><td>Utrecht University</td><td>Netherlands</td><td>The Johns Hopkins University</td><td>USA</td></tr><tr><td>The University of Queensland</td><td>Australia</td><td>VU University Amsterdam</td><td>Netherlands</td><td>The Ohio State University Columbus</td><td>USA</td></tr><tr><td>The University of Western Australia</td><td>Australia</td><td>University of Oslo</td><td>Norway</td><td>University of California Davis</td><td>USA</td></tr><tr><td>University of New South Wales</td><td>Australia</td><td>University of Basel</td><td>Switzerland</td><td>University of California Irvine</td><td>USA</td></tr><tr><td>University of Sydney</td><td>Australia</td><td>University of Geneva</td><td>Switzerland</td><td>University of California Los Angeles</td><td>USA</td></tr><tr><td>Ghent University</td><td>Belgium</td><td>University of Zurich</td><td>Switzerland</td><td>University of California San Diego</td><td>USA</td></tr><tr><td>KU Leuven</td><td>Belgium</td><td>King's College London</td><td>UK</td><td>University of Chicago</td><td>USA</td></tr><tr><td>McGill University</td><td>Canada</td><td>The Imperial College of Science Technology and Medicine</td><td>UK</td><td>University of Florida</td><td>USA</td></tr><tr><td>McMaster University</td><td>Canada</td><td>The University of Edinburgh</td><td>UK</td><td>University of Michigan</td><td>USA</td></tr><tr><td>University of Alberta</td><td>Canada</td><td>The University of Manchester</td><td>UK</td><td>University of Minnesota</td><td>USA</td></tr><tr><td>University of British Columbia</td><td>Canada</td><td>University College London</td><td>UK</td><td>University of North Carolina</td><td>USA</td></tr><tr><td>University of Toronto</td><td>Canada</td><td>University of Nottingham</td><td>UK</td><td>University of Pennsylvania</td><td>USA</td></tr><tr><td>Aarhus University</td><td>Denmark</td><td>University of Oxford</td><td>UK</td><td>University of Pittsburgh</td><td>USA</td></tr><tr><td>University of Copenhagen</td><td>Denmark</td><td>Boston University</td><td>USA</td><td>University of Rochester</td><td>USA</td></tr><tr><td>University of Helsinki</td><td>Finland</td><td>Brown University</td><td>USA</td><td>University of Southern California</td><td>USA</td></tr><tr><td>Heidelberg University</td><td>Germany</td><td>Case Western Reserve University</td><td>USA</td><td>University of Utah</td><td>USA</td></tr><tr><td>Humboldt University Berlin</td><td>Germany</td><td>Columbia University</td><td>USA</td><td>University of Virginia</td><td>USA</td></tr><tr><td>University of Bonn</td><td>Germany</td><td>Cornell University</td><td>USA</td><td>University of Washington</td><td>USA</td></tr><tr><td>University of Munich</td><td>Germany</td><td>Duke University</td><td>USA</td><td>University of Wisconsin</td><td>USA</td></tr><tr><td>Erasmus University Rotterdam</td><td>Netherlands</td><td>Emory University</td><td>USA</td><td>Vanderbilt University</td><td>USA</td></tr><tr><td>Leiden University</td><td>Netherlands</td><td>Michigan State University</td><td>USA</td><td>Washington University in St Louis</td><td>USA</td></tr><tr><td>Radboud University Nijmegen</td><td>Netherlands</td><td>New York University</td><td>USA</td><td>Yale University</td><td>USA</td></tr><tr><td>University of Amsterdam</td><td>Netherlands</td><td>Northwestern University</td><td>USA</td><td /><td /></tr><tr><td>Techno-Science Universities</td></tr><tr><td>Tsinghua University</td><td>China</td><td>Carnegie Mellon University</td><td>USA</td><td>University of California Santa Barbara</td><td>USA</td></tr><tr><td>Ecole Normale Superieure Paris</td><td>France</td><td>Georgia Institute of Technology</td><td>USA</td><td>University of California Santa Cruz</td><td>USA</td></tr><tr><td>Nanyang Technological University</td><td>Singapore</td><td>Massachusetts Institute of Technology MIT</td><td>USA</td><td>University of Colorado</td><td>USA</td></tr><tr><td>Swiss Federal Institute of Technology Lausanne</td><td>Switzerland</td><td>Princeton University</td><td>USA</td><td>University of Maryland</td><td>USA</td></tr><tr><td>Swiss Federal Institute of Technology Zurich</td><td>Switzerland</td><td>Rice University</td><td>USA</td><td>University of Texas</td><td>USA</td></tr><tr><td>California Institute of Technology</td><td>USA</td><td>University of California Berkeley</td><td>USA</td><td /><td /></tr><tr><td>Traditional science universities</td></tr><tr><td>The Australian National University</td><td>Australia</td><td>Kyoto University</td><td>Japan</td><td>Arizona State University</td><td>USA</td></tr><tr><td>Fudan University</td><td>China</td><td>Osaka University</td><td>Japan</td><td>Pennsylvania State University</td><td>USA</td></tr><tr><td>Peking University</td><td>China</td><td>The University of Tokyo</td><td>Japan</td><td>Purdue University</td><td>USA</td></tr><tr><td>Shanghai Jiao Tong University</td><td>China</td><td>National University Singapore</td><td>Singapore</td><td>Rutgers University</td><td>USA</td></tr><tr><td>Zhejiang University</td><td>China</td><td>Seoul National University</td><td>South Korea</td><td>Texas A&M University</td><td>USA</td></tr><tr><td>Pierre and Marie Curie University Paris_6</td><td>France</td><td>Stockholm University</td><td>Sweden</td><td>University of Arizona</td><td>USA</td></tr><tr><td>University of Paris Sud Paris_11</td><td>France</td><td>National Taiwan University</td><td>Taiwan</td><td>University of Illinois</td><td>USA</td></tr><tr><td>Technical University Munich</td><td>Germany</td><td>University of Bristol</td><td>UK</td><td /><td /></tr><tr><td>Technion-Israel Institute of Technology</td><td>Israel</td><td>University of Cambridge</td><td>UK</td><td /><td /></tr><tr><td>Harvard</td><td /><td>Harvard University</td><td>USA</td><td /><td /></tr></tbody></table> </ephtml> </p> <ref id="AN0140974192-22"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref66" type="bt">1</bibl> <bibtext> University administration in many systems has the power to reallocate at least a portion of their resources to priority areas. 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  Data: Ordering the Global Field of Academic Science: Money, Mission, and Position
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  Data: <searchLink fieldCode="AR" term="%22Cantwell%2C+Brendan%22">Cantwell, Brendan</searchLink><br /><searchLink fieldCode="AR" term="%22Taylor%2C+Barrett+J%2E%22">Taylor, Barrett J.</searchLink><br /><searchLink fieldCode="AR" term="%22Johnson%2C+Nathan+M%2E%22">Johnson, Nathan M.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Studies+in+Higher+Education%22"><i>Studies in Higher Education</i></searchLink>. 2020 45(1):18-33.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 16
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  Data: 2020
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  Data: Journal Articles<br />Reports - Research
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  Label: Education Level
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Global+Approach%22">Global Approach</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Universities%22">Research Universities</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+Research%22">Scientific Research</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+Regions%22">Geographic Regions</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Reputation%22">Reputation</searchLink><br /><searchLink fieldCode="DE" term="%22Cross+Cultural+Studies%22">Cross Cultural Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Resources%22">Educational Resources</searchLink><br /><searchLink fieldCode="DE" term="%22Competition%22">Competition</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Education%22">Comparative Education</searchLink><br /><searchLink fieldCode="DE" term="%22Faculty+Publishing%22">Faculty Publishing</searchLink><br /><searchLink fieldCode="DE" term="%22Institutional+Mission%22">Institutional Mission</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Institutional+Evaluation%22">Institutional Evaluation</searchLink>
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  Data: 10.1080/03075079.2018.1506916
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  Data: 0307-5079
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Researchers have identified the emergence of a global field of academic science. In order to understand the dynamics of this field, this study used latent profile and regression techniques to analyze data gathered for a sample of 114 research universities from around the world. Sociological theory informed the framing and conceptualization of the study. Results demonstrated that leading research universities emphasized different areas of science, that science emphasis was patterned by geographic region, and that region, resource levels, and science emphasis all predicted status in the field. Implications for theory and future research were discussed.
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        Value: 10.1080/03075079.2018.1506916
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 18
    Subjects:
      – SubjectFull: Global Approach
        Type: general
      – SubjectFull: Research Universities
        Type: general
      – SubjectFull: Scientific Research
        Type: general
      – SubjectFull: Geographic Regions
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Reputation
        Type: general
      – SubjectFull: Cross Cultural Studies
        Type: general
      – SubjectFull: Science Education
        Type: general
      – SubjectFull: Educational Resources
        Type: general
      – SubjectFull: Competition
        Type: general
      – SubjectFull: Comparative Education
        Type: general
      – SubjectFull: Faculty Publishing
        Type: general
      – SubjectFull: Institutional Mission
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Institutional Evaluation
        Type: general
    Titles:
      – TitleFull: Ordering the Global Field of Academic Science: Money, Mission, and Position
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Cantwell, Brendan
      – PersonEntity:
          Name:
            NameFull: Taylor, Barrett J.
      – PersonEntity:
          Name:
            NameFull: Johnson, Nathan M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 0307-5079
          Numbering:
            – Type: volume
              Value: 45
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Studies in Higher Education
              Type: main
ResultId 1