Power, brokers, and agendas: New directions for the use of social network analysis in education policy Journal website: http://epaa.asu.edu/ojs/ Manuscript received: 8/3/2020 Facebook: /EPAAA Revisions received: 8/5/2020 Twitter: @epaa_aape Accepted: 8/5/2020 SPECIAL ISSUE Researching 21st Century Education Policy Through Social Network Analysis education policy analysis archives A peer-reviewed, independent, open access, multilingual journal Arizona State University Volume 28 Number 117 August 17, 2020 ISSN 1068-2341 Power, Brokers, and Agendas: New Directions for the Use of Social Network Analysis in Education Policy Emily Hodge Montclair State University United States Joshua Childs The University of Texas at Austin United States Wayne Au University of Washington, Bothell United States Citation: Hodge, E., Childs, J., & Au, W. (2020). Power, brokers, and agendas: New directions for the use of social network analysis in education policy. Education Policy Analysis Archives, 28(117). https://doi.org/10.14507/epaa.28.5874 This article is part of the special issue, Researching 21st Century Education Policy Through Social Network Analysis, guested edited by Emily Hodge, Joshua Childs, and Wayne Au. Abstract: In this special issue, Researching 21st Century Education Policy Through Social Network Analysis, authors use social network analysis (SNA) to explore policy networks, broaden the current literature of sociological approaches to SNA, and/or incorporate new lenses for interpreting policy networks from political science or other academic disciplines. This editorial introduction first provides an http://epaa.asu.edu/ojs/ https://doi.org/10.14507/epaa.28.5874 Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 2 overview of policy networks and their relevance in education. Then, the editors describe existing work applying the tools of SNA to education policy and highlight understudied areas before describing the articles included in this issue. These articles apply SNA to a variety of education policy issues, including large scale policies such as the Every Student Succeeds Act and the Common Core State Standards, charter schools, and the relationship between system and non-system actors. Articles highlight multiple applications of SNA, including how SNA can be used to advance theory, as well as describe and predict policy networks. Keywords: social network analysis; policy networks; education policy; organizations Poder, brokers y agendas: Nuevas direcciones para el uso del análisis de redes sociales en la política educativa Resumen: En este número especial, Investigando la Política Educativa del Siglo XXI a través del Análisis de Redes Sociales, los autores utilizan el análisis de redes sociales (SNA) para explorar redes de políticas, ampliar la literatura actual sobre enfoques sociológicos del SNA y / o incorporar nuevas lentes para interpretar las redes de políticas. de la ciencia política u otras disciplinas académicas. Esta introducción editorial proporciona en primer lugar una descripción general de las redes de políticas y su relevancia en la educación. Luego, los editores describen el trabajo existente aplicando las herramientas del SNA a la política educativa y resaltan áreas poco estudiadas antes de describir los artículos incluidos en este número. Estos artículos aplican el SNA a una variedad de cuestiones de política educativa, incluidas políticas a gran escala como la Every Student Succeeds Act and the Common Core State Standards, las escuelas autónomas y la relación entre los actores del sistema y los que no pertenecen al sistema. Los artículos destacan múltiples aplicaciones del SNA, incluida la forma en que el SNA se puede utilizar para promover la teoría, así como para describir y predecir redes de políticas. Palabras-clave: análisis de redes sociales; redes de políticas; política educativa; organizaciones Poder, brokers e agendas: Novas direções para o uso da análise de redes sociais na política educacional Resumo: Nesta dossiê, Pesquisando Políticas Educacionais do Século 21 através da Análise de Redes Sociais, os autores usam a análise de redes sociais (SNA) para explorar as redes de políticas, ampliar a literatura atual de abordagens sociológicas para SNA e / ou incorporar novas lentes para interpretar as redes de políticas de ciências políticas ou outras disciplinas acadêmicas. Esta introdução editorial fornece primeiro uma visão geral das redes de políticas e sua relevância na educação. Em seguida, os editores descrevem o trabalho existente aplicando as ferramentas do SNA à política educacional e destacam áreas pouco estudadas antes de descrever os artigos incluídos neste número. Esses artigos aplicam SNA a uma variedade de questões de política educacional, incluindo políticas de grande escala, como a Every Student Succeeds Act and the Common Core State Standards, escolas licenciadas e a relação entre atores do sistema e não -sistema. Os artigos destacam várias aplicações do SNA, incluindo como o SNA pode ser usado para avançar a teoria, bem como descrever e prever redes de políticas. Palavras-chave: análise de redes sociais; redes de políticas; política educacional; organizações Power, brokers, and agendas: New directions for the use of social network analysis in education policy 3 Policy Networks and Networked Governance Over the last several decades, public policy has increasingly involved a complex web of actors. What used to be a government-led effort to legislating and implementing policy has now become an expanded enterprise composed of a nebulous array of individuals, non-system actors, non-governmental organizations, philanthropies, and corporations (Bevir & Richards, 2009; Castells, 1996; Eggers & Goldsmith, 2003). In addition to increased recognition of this “networked governance” perspective as part of policy making and implementation, COVID-19’s recent disruption to numerous social sectors and the response that followed has brought to the forefront the importance of network approaches for effectively addressing national crises. Emerging networks to secure personal protective equipment, adequately implement national testing, and facilitate supply chains are of critical importance (Ivanov, 2020; Kavi, 2020; Reuters, 2020). Further, repeated calls to dismantle systems of racial inequality and oppression have given rise to networks using resources and expertise to support communities and organizations of color (Abolitionist Teaching Network, 2020; Black Lives Matter, 2020; Education for Liberation Network, 2020). The concept of policy networks developed in the social sciences in the 1970s and 1980s as a response to a growing number of actors influencing the public policy-making process (Kenis & Schneider, 1991). Policy networks “consist of governmental and societal actors whose interactions with one another give rise to policies” (Bevir & Richards, 2009). In recent decades, collaborative policy networks have become an increasingly important means of service provision and governance (Agranoff & McGuire, 1999; DeLeon & Varda, 2009; O’Toole, 1997). Such networks are constituted by relationships among public agencies, advocacy groups, nonprofits, and for-profit firms that are active within a particular policy area and at times engage in joint projects to achieve shared goals (DeLeon & Varda, 2009; Hatmaker & Rethemeyer, 2008). Policy networks are particularly likely to arise in situations in which problems are characterized as “wicked”, when there are multiple stakeholders and organizations acting alone cannot adequately achieve their goals (DeLeon & Varda, 2009; O’Toole, 1997). Given their increasing importance to governance and policy outcomes, researchers have sought to understand the ways in which policy networks form, evolve, and dissolve, and how policy networks serve political ends and influence the policy process. Research on policy networks is based on a tradition in political science that focuses on the actors who participate in policy decision-making. This stream of work originated in the 1960s and was adopted in British research on policy communities and networks in the 1980s and 1990s (Marsh, 1998; Marsh & Rhodes, 1992; Rhodes, 1990). The American and European literature on policy networks differ in topic and methodology: traditionally, researchers in Great Britain and Europe conducted policy group studies (Marsh & Rhodes, 1992; Richardson & Jordan, 1979), while U.S. researchers have relied on quantitative methods or social network analysis (SNA) to study policy formation and diffusion (Freeman & Stevens, 1987; Laumann, Knoke, & Kim, 1985). These traditions have few links, and authors subscribing to one do not generally reference the work of those subscribing to the other (Marcussen & Olsen, 2007). While SNA has been an increasingly popular set of tools applied across many disciplines, in education, researchers have generally used SNA from a sociological perspective rather than a political science lens. Similarly, others have conducted research on policy networks in education, but not often with the tools of SNA. In this special issue, authors were asked to use SNA to explore policy networks, broaden the current literature of sociological approaches to SNA, and/or incorporate new lenses for interpreting policy networks from political science or other academic disciplines. Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 4 Policy Networks and Social Network Analysis in Educational Research In educational research, policy network theories have most extensively been applied by Stephen Ball, who has examined how philanthropic and business interests converged around the narrative that education is in need of reform and that a wide array of organizational actors outside the traditional system actors at the federal, state, and district level must be engaged to provide those solutions. Ball and colleagues investigate the organizational actors connected to initiatives like low- cost private schools and digital curriculum materials across the globe (e.g., Ball & Junemann, 2012). Ball most often uses “network ethnography”, an approach combining information from internet searches with policy actor interviews in order to illuminate ties between organizations based on shared funding, policy positions, flow of information, etc. (Ball, 2016; Ball et al., 2017). These relationships are often visualized in a sociogram or similar type of diagram. Similarly, Au and Ferrare’s (2015) edited volume provides a number of examples of the connections between organizations involved in global neoliberal reform. Other studies of policy networks in the US have focused on creating typologies of the intermediary organizations involved in advocacy and communication of educational research (Scott et al., 2014), conceptual frameworks for how philanthropies interact with various intermediary organizations (Scott & Jabbar, 2014), and how networks of intermediary organizations coalesce to advocate for particular policy positions (Lubienski, 2018). These studies have illuminated relationships between various organizations and the policy process, but they have not often used the tools of SNA to directly examine organizational networks influencing policy. Many of the education studies using SNA have asked sociologically oriented questions, usually centered around exploring social capital and its ties to social networks. Prior studies have applied SNA to examine the role of social capital and interpersonal relationships in educational reform (e.g., Coburn & Russell, 2008; Daly & Finnigan, 2010, 2011; Finnigan et al., 2013; Liou, 2016; Moolenaar et al., 2010). These studies, and many others, have provided critical insight into the role of advice-seeking, social capital, and trust within schools and districts, and how actors within given organizational contexts facilitate the enactment of specific reforms through network connections or positions. Understanding how actors interact with one another, and how actors engage in a variety of activities over policy aims and measures, occurs in the context of institutional or collective arrangements in which the policy process unfolds (e.g., Howlett et al., 2017). However, key questions remain for understanding the role and impact of policy networks in educational systems, and how actors within these networks leverage expertise and resources to influence policy outcomes. SNA is a powerful tool that allows researchers to visualize, for example, coalitions of organizations involved in advocating for particular policies (e.g., Au & Ferrare, 2014), organizations with shared positions on policy, and the attributes that predict shared policy positions. Further, using SNA to study policy networks illuminates the relationship between policy and politics, or “contestation over values, ideas, ideological, and material interests” (Saltman, 2014) that manifests in public policy. SNA offers one approach for understanding the politics and power relationships embedded within policy networks because it provides a way of visualizing networks of influence and power across organizations. SNA allows one to understand social relations and how embedded actors influence an overall network, dissemination of information, and policy. As education researchers have expanded their understanding of school and schooling, so too has their focus shifted to a deeper conceptualization of organizational (and personal) relationships that impact educational outcomes (Daly, 2010). Governance and networks have become buzzwords in policy analysis that have been incorporated in educational research to reflect contemporary changes in Power, brokers, and agendas: New directions for the use of social network analysis in education policy 5 politics and the state. Identifying the key actors in policy networks, how they are formed, interaction within and between networks, and the impact of those interactions on policy, have influenced education policy research in recent years (Daly, 2010; Howlett et al., 2017; Lubienski, 2019; Schuster et al., 2019). SNA has been used to describe relationships between organizations, whether foundations, state education agencies, intermediary organizations, or various not-for-profit and for-profit entities, providing powerful illustrations of policy networks. Over the last 10 years, groundbreaking work in educational research used SNA to make the convergence in philanthropic giving explicit, both in terms of specific organizations supported and foundations’ similar priorities for market-based educational reforms (Reckhow, 2013; Reckhow & Snyder, 2014) and the funding networks of alternative teacher certification and charter schools (Au & Ferrare, 2014; Ferrare & Reynolds, 2016; Kretchmar et al., 2014). Others have demonstrated how states responded to the federal incentives of the Race to the Top competition by assembling complex interorganizational networks (Russell et al., 2015). Descriptive studies are useful for highlighting the importance of networks in designing and implementing policies, while also providing insight on how these networks are formed and used. For example, Au and Ferrare (2014) used SNA to identify the individuals and organizations involved in funding charter school legislation in Washington state. Hodge, Salloum, and Benko (2016) visualized the connections between state-provided curricular resources and their organizational sponsors to understand the organizations influencing the implementation of the Common Core State Standards (CCSS). Miskel and Song’s work provided a pioneering use of SNA in literacy research to visualize coalitions influencing literacy policy at the federal level (Miskel & Song, 2004) and state level (Song & Miskel, 2005, 2007). More recent network-focused literacy research examined the collective capacity of external providers’ reading programs in New York City (Hatch et al., 2019) and the similarities and differences in organizations’ messages about literacy instruction (Hodge et al., 2020). SNA studies combining description and prediction are becoming more common in education, often utilizing new tools and software for SNA. For example, the Discourse Network Analyzer (Leifeld, 2013) is a tool developed by a political scientist that allows text to be coded for particular ideas and the individual/organization putting forward that idea, and then exported to social network software. A recent set of studies applied this tool to congressional hearings to identify actors with shared policy preferences on teacher quality and to identify predictors of shared policy preferences (e.g., Galey-Horn et al., 2019; Reckow & Tompkins-Stange, 2018). Predictive analysis is useful for forecasting how information will spread within a network, the environments for networks to emerge, and the influence of social capital (Fernandes et al., 2019; Williamson, 2016). Studies that have used educational data mining techniques to predict network outcomes provide an understanding of the strength of ties and density of policy networks (Regan & Khwaja, 2019). Other studies draw on large scale social media data to describe and predict discourse about education policy, including opt-outs (Paquin Morel, 2019), the CCSS (Supovitz et al., 2017; Wang & Fikis, 2017), and the Every Student Succeeds Act (ESSA; Curran & Kellogg, 2017). Because of the potential for SNA as a tool for understanding and predicting policy networks, this special issue provided an outlet for scholars engaged in the next generation of studies using SNA in innovative ways to describe and predict a variety of policies across problem definition, formation, and implementation. Structure of the Special Issue In this special issue, we feature manuscripts applying the tools of SNA to policy networks involved in a variety of education policy issues in the current political climate. Some are related to Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 6 large scale policies, such as ESSA implementation (Wang) or the Common Core State Standards (CCSS). Two papers examine how the combination of the CCSS and Race to the Top influenced educational policy—one identifies state attributes associated with states endorsing curricular and instructional resources from particular organizations (Salloum, Hodge, and Benko), and the other examines social movements around opting out of large-scale assessments linked to the CCSS (Green Saraisky and Pizmony-Levy). Two papers examine aspects related to charter schools. Castillo identifies the strategies progressive charter schools use to fund themselves, while David and colleagues identify the clusters of STEM coursework offered in charter schools in Texas, asking about students’ opportunity to learn in charter vs. non-charter public schools. Regardless of topic, many of these papers ask about the relationship between system and non-system actors, consistent with the networked governance perspective of many policy network studies. Haddad asks about foundation funding for higher education, identifying foundations’ priorities through a set of interviews and looking at the role of intermediary organizations in coordinating reform priorities. Oliveira and Daroit’s paper on the Bolsa Familía program in Brazil theorizes about the relationship between system and non-system actors when families can only receive funds from this social services program when children attend school regularly. These articles are grouped by their theoretical and methodological contribution, and we provide an overview of each below. We begin with an article from Sarah Galey-Horn and Joseph Ferrare outlining a set of related theories for understanding policy change as related to social network concepts. We then move into network studies that take a descriptive approach and end with those combining both descriptive and predictive approaches. Galey-Horn and Ferrare’s theoretical contribution, “Using Policy Network Analysis to Understand Ideological Convergence and Change in Educational Subsystems,” anchors the special issue. The manuscript outlines a more refined and specific approach to policy network analysis in education, focused in particular on a unified, explanatory framework for understanding the role of ideas and beliefs in policy change and formation. Galey-Horn and Ferrare describe three theoretical frameworks useful for a network approach to studying policy change: Advocacy Coalition Framework, Discourse Network Analysis, and Argumentation Discourse Analysis Approach. Each offers a distinct contribution to understanding how particular education policies become prominent on the policy agenda. Advocacy Coalition Framework proposes a three-layer hierarchical belief system undergirding policy change: coalitions emerge around particular sets of specific policy preferences when policy actors have shared, “deep core beliefs” in values like efficiency or choice, and shared viewpoints on how those core beliefs should be enacted in policy. Discourse Network Analysis provides a way of making the implicit beliefs from advocacy coalition framework explicit, coding texts such as articles, testimony, mission statements, websites, etc. for evidence of policy preferences and their corresponding policy core beliefs and deep core beliefs. Then, the ties between actors and various policy preferences can be placed in a matrix for visualization and analysis using SNA. The final theoretical perspective, Argumentation Discourse Analysis Approach, emphasizes “storylines” or “policy narratives,” viewing coalitions as formed not only of people with shared beliefs and policy preferences, but shared stories about policy problems and solutions. Then, Galey and Farrare offer examples of how these complementary frameworks can be combined to understand market-based policy changes, using the cases of foundations’ shared funding priorities around alternative certification programs and charter schools; intermediary organizations’ use of research to promote urban charter schools; and policy entrepreneurs arguing for how the CCSS would promote greater efficiency in education. Further illustrating some of these ideas, Yinying Wang’s manuscript, “Understanding Congressional Coalitions: A Discourse Network Analysis of Congressional Hearings for the Every Power, brokers, and agendas: New directions for the use of social network analysis in education policy 7 Student Succeeds Act,” uses Advocacy Coalition Framework and Discourse Network Analysis to understand the coalitions active in ESSA implementation. Wang coded testimony from hearings on ESSA implementation between 2016 and 2018, recording the policy actor providing testimony, their organizational affiliation, and the claims the policy actors made about ESSA implementation. Wang finds that eight categories of actors provided testimony, including those from the federal and state levels, teachers’ unions, interest groups, district leaders, and teachers. Qualitative coding of policy claims suggested four overall coalitions around the issues of equity, assessment and accountability, how states have responded to ESSA in their legislation, and the inconsistent state approvals from the U.S. Department of Education. As described above, one line of policy network research has been spearheaded by Stephen Ball, who combines a networked governance perspective with an approach similar to SNA that he calls network ethnography. Ball’s work has been particularly influential in Brazil (Mainardes & Gandin, 2013), and Breynner Ricardo Oliveira and Doriana Daroit’s article, “Public Policy Networks and the Implementation of the Bolsa-Família Program: An Analysis Based on the Monitoring of School Attendance” takes a similar approach to understanding how local actors make sense of a Brazilian welfare program, the “Bolsa Família Program.” Oliveira and Daroit interviewed individuals at various levels of the program’s administration, from those involved at the federal level in the program’s strategic direction, to those at the local level in both schools and social services. The funds that families receive are conditional, based on students meeting school attendance benchmarks, and the authors identify a complex interorganizational network and routines developed across governance levels to monitor attendance. Elise Castillo’s piece, “Doing What It Takes to Keep the School Open”: The Philanthropic Networks of Progressive Charter Schools” examines the strategies that leaders of progressive charter schools use to secure financial support. These schools often deliberately set their pedagogical approaches in contrast to those used in no-excuses Charter Management Organizations (CMOs). However, Castillo finds that these schools had to draw from the playbook of larger, market-based charter networks to secure their financial health—recruiting wealthy board members with deep- pocketed social networks, drawing on the social networks of parents, teachers, and leaders for financial support, and accepting some foundation funding. While Castillo does not use a formal SNA approach, her work is similarly grounded in policy networks (Ball, 2008), identifying the connections between philanthropy, think-tanks, and business as critical to the growth of many charter schools and networks. However, despite growing criticism of some CMOs and a policy window opening for charters taking more progressive pedagogical approaches, little was known before this study about how these schools sought out primary resources to establish themselves and transactional resources to maintain their existence (Rowan, 2002). Castillo’s work demonstrates how these schools’ financial health rests upon the social capital of those affiliated with the school and school personnel’s ability to leverage those connections into concrete financial resources. Nabih Haddad extends earlier findings about the role of foundation funding in K–12 (e.g., Reckhow, 2013; Reckhow & Snyder, 2014) to higher education in “Foundation-sponsored Networks: Brokerage Roles of Higher Education Intermediary Organizations.” Haddad identifies funding networks through a combination of interviews with foundation officials and grant recipients, as well as SNA of prominent higher education funders. Haddad also points out, as have Scott and Jabbar (2014) and others, that foundations do not simply disburse money to grantees to disseminate reform ideas and priorities. Instead, they coordinate with intermediary organizations to broker knowledge about reforms. Indeed, Haddad finds that higher education funders convene organizational networks to support their priorities and that foundations prioritize funding those whom they know will be able to broker ideas and relationships across multiple sectors, like Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 8 membership organizations, advocacy organizations, and system actors like statewide higher education systems who are broadly connected in higher education. Moving from description to prediction, Bernard David, Michael Marder, Jill Marshall, and María González-Howard’s piece, “How Do Students Experience Choice? Exploring STEM Course- offerings and Course-taking Patterns in Texas Charter and Non-charter Public Schools” investigates charter schools and STEM course-taking. The authors point out that a frequent rationale for charter schools is that the more flexible governance format could allow for innovation in curriculum and instruction. Assuming that broadened STEM course-taking is a desirable outcome, the authors use SNA to analyze state administrative data on student course-taking to investigate whether students are taking more STEM courses in charter schools than in non-charter public schools. David and colleagues’ paper represents how SNA, as a tool to represent relationships, can be used flexibly. The authors construct several two-mode networks that they convert into one-mode networks for analysis, as well as a community detection algorithm and multiple quantitative models, to identify whether charters in Texas are offering (and students are taking) different STEM coursework than non-charters. The authors find that charters are less likely to offer STEM classes earmarked for students receiving special education services or to offer intermediate-level courses they call “college preparatory.” Charter schools are more likely to have students taking sets of STEM coursework they identify as “advanced” or “basic,” as well as to have students who transfer out of the charter school and those who drop out. These findings raise questions about school organization in terms of how schools are construing students’ needs and organizing coursework to meet those perceived needs. Further, the authors use SNA in ways that are both descriptive and predictive. Many of the pieces in this special issue and in the policy networks in education literature generally examine the underlying communication, funding, or ideological networks of powerful elites (individual or organizational) shaping policy. Nancy Green Saraisky and Oren Pizmony-Levy’s piece provides a unique contribution in that it examines grassroots activists— the parents and caregivers opting children out of state tests (generally assessments adopted to measure the CCSS). The authors draw on social movement theory, asking about the extent to which those who opt-out are acting as individuals or have contact with various organizations that might be shaping their positions. The authors draw on two large scale surveys of opt-out participants at different timepoints, visualizing affiliation networks at each time point of the organizations who had contacted participants (i.e., two organizations are connected when one or more respondents reported that they were contacted by both organizations). They find that while national organizations’ activity remained strong, only state- level organizations in the Northeast continued to mobilize between 2016 and 2018. Those in other areas reduced their activism by 2018. Both liberals and conservatives were more likely to be contacted by opt-out organizations in 2016, reflecting the transpartisan coalitions of resistance to the CCSS and related assessments, though fewer conservatives were contacted in 2018. The authors also conduct logistic regression analyses to identify the extent to which having contact with an organization in the opt-out network relates to survey respondents’ likelihood of having particular attitudes towards reform. The authors provide some suggestive evidence that opt-out supporters who had contact with social movement organizations were less likely to see common expectations for student learning (i.e., standards) as very important. Additionally, respondents who had contact with social movement organizations were more likely to view education as the proper responsibility of the local and state levels, rather than the federal government. Serena Salloum, Emily Hodge, and Susanna Benko’s piece also investigates CCSS networks, but of state-provided resources rather than individuals. Like the David et al. and Green Saraisky and Pizmony-Levy pieces, it both describes and predicts. The first part of the study is a descriptive SNA of the organizations sponsoring state-provided resources for English language arts teachers, and the Power, brokers, and agendas: New directions for the use of social network analysis in education policy 9 second part uses a regression model appropriate for network-based data to understand the characteristics associated with states turning to the same organizations. Based on the idea that there was widespread uncertainty in the policy environment in the wake of CCSS adoption, Salloum and colleagues use an institutional theory lens to examine attributes associated with pairs of states having shared organizational ties (DiMaggio & Powell, 1983). The authors use multiple regression quadratic assignment procedure, a quantitative model that takes into account network data’s interdependencies, to understand how various state attributes representing isomorphic change processes (e.g., adopting the CCSS, geographic region, and the degree of local control over curriculum, among others) are related to the number of shared organizations that states turn to for information about standards. The authors find that both RTTT application and CCSS adoption are related to states turning to similar numbers of shared organizations. These variables represent coercive isomorphism, providing evidence that applying for RTTT not only influenced CCSS adoption, but also shaped how states approached CCSS implementation in the guidance provided for teachers. Together, these articles provide models for how to use SNA to describe and predict education policy networks. The articles also suggest new theoretical approaches outside of traditional sociological approaches to SNA, including those from political science as well as institutional theory and social movement theory. In addition, Galey-Horn and Ferrare offer a set of theoretical approaches from political science to apply to education policy networks. As others continue to innovate in their methodological approaches to SNA and analyze networks through various conceptual and theoretical frameworks, research will be needed to broaden our understanding of policy networks. The next wave of SNA research has the potential to help us more deeply explore agendas and power relationships in policy formation and implementation, as well as the influence of brokers on policy ideas. References Abolitionist Teaching Network. (2020). Homepage. https://abolitionistteachingnetwork.org Agranoff, R., & McGuire, M. (1999). Managing in network settings. Review of Policy Research, 16(1), 18–41. https://doi.org/10.1111/j.1541-1338.1999.tb00839.x Au, W., & Ferrare, J. J. (2014). Sponsors of policy: A network analysis of wealthy elites, their affiliated philanthropies, and charter school reform in Washington state. Teachers College Record, 116(11), 1–24. Ball, S. J (2008). New philanthropy, new networks and new governance in education. Political Studies, 56(4), 747–765. https://doi.org/10.1111/j.1467-9248.2008.00722.x Ball, S. J (2016). Following policy: Networks, network ethnography and education policy mobilities. Journal of Education Policy, 31(5), 549–566. https://doi.org/10.1080/02680939.2015.1122232 Ball, S. J., & Junemann, C. (2012). Networks, new governance and education. Policy Press. https://doi.org/10.1332/policypress/9781847429803.001.0001 Ball, S. J., Junemann, C., & Santori, D. (2017). Edu. net: Globalisation and education policy mobility. Taylor & Francis. https://doi.org/10.4324/9781315630717 Bevir, M., & Richards, D. (2009). Decentring policy networks: A theoretical agenda. Public Administration, 87(1), 3–14. https://doi.org/10.1111/j.1467-9299.2008.01736.x Black Lives Matter. (2020). What we believe. https://blacklivesmatter.com/what-we-believe/ Castells, M. (1996). The information age: Economy, society and culture: Vol. I. The rise of network society. Blackwell. https://doi.org/10.1111/j.1541-1338.1999.tb00839.x https://doi.org/10.1111/j.1467-9248.2008.00722.x https://doi.org/10.1080/02680939.2015.1122232 https://doi.org/10.1332/policypress/9781847429803.001.0001 https://doi.org/10.4324/9781315630717 https://doi.org/10.1111/j.1467-9299.2008.01736.x https://blacklivesmatter.com/what-we-believe/ Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 10 Coburn, C. E., & Russell, J. L. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30(3), 203–235. https://doi.org/10.3102/0162373708321829 Curran, F. C., & Kellogg, A. T. (2017). Sense-making of federal education policy: Social network analysis of social media discourse around the Every Student Succeeds Act. Journal of School Leadership, 27(5), 622–651. https://doi.org/10.1177/105268461702700502 Daly, A. J. (Ed). (2010) Social network theory and educational change. Harvard Education Press. Daly, A. J., & Finnigan, K. S. (2010). A bridge between worlds: Understanding network structure to understand change strategy. Journal of Educational Change, 11(2), 111–138. https://doi.org/10.1007/s10833-009-9102-5 Daly, A. J., & Finnigan, K. S. (2011). The ebb and flow of social network ties between district leaders under high-stakes accountability. American Educational Research Journal, 48(1), 39–79. https://doi.org/10.3102/0002831210368990 DeLeon P., & Varda, D. M. (2009). Toward a theory of collaborative policy networks: Identifying structural tendencies. Policy Studies Journal, 37(1), 59–74. https://doi.org/10.1111/j.1541- 0072.2008.00295.x DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101 Education for Liberation Network. (2020). Our vision. https://www.edliberation.org/about-us/our- vision/ Eggers, W., & Goldsmith, S. (2003). Networked government. Government Executive, 35(7), 28–33. Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012 Ferrare, J. J., & Reynolds, K. (2016). Has the elite foundation agenda spread beyond the Gates? An organizational network analysis of nonmajor philanthropic giving in K–12 education. American Journal of Education, 123(1), 137–169. https://doi.org/10.1086/688165 Finnigan, K. S., Daly, A. J., & Che, J. (2013). Systemwide reform in districts under pressure: The role of social networks in defining, acquiring, using, and diffusing research evidence. Journal of Educational Administration, 51(4), 476–497. https://doi.org/10.1108/09578231311325668 Freeman, J. L., & Stevens, J. P. (1987). A theoretical and conceptual reexamination of subsystem politics. Public Policy and Administration, 2(1), 9–24. https://doi.org/10.1177/095207678700200102 Galey-Horn, S., Reckhow, S., Ferrare, J. J., & Jasny, L. (2020). Building consensus: Idea brokerage in teacher policy networks. American Educational Research Journal, 57(2), 872–905. https://doi.org/10.3102/0002831219872738 Hatch, T., Ahn, M., Ferguson, D., & Rumberger, A. (2019). The role of external support in improving K–3 reading outcomes in New York City. Urban Education. https://doi.org/10.1177/0042085919877932 Hatmaker, D. M., & Rethemeyer, R. K. (2008). Mobile trust, enacted relationships: Social capital in a state-level policy network. International Public Management Journal, 11(4), 426–462. https://doi.org/10.1080/10967490802494867 Hodge, E. M., Salloum, S. J., & Benko, S. L. (2016). (Un) commonly connected: A social network analysis of state standards resources for English/Language Arts. AERA Open, 2(4). https://doi.org/10.1177/2332858416674901 https://doi.org/10.3102/0162373708321829 https://doi.org/10.1177/105268461702700502 https://doi.org/10.1007/s10833-009-9102-5 https://doi.org/10.3102/0002831210368990 https://doi.org/10.1111/j.1541-0072.2008.00295.x https://doi.org/10.1111/j.1541-0072.2008.00295.x https://doi.org/10.2307/2095101 https://doi.org/10.1016/j.jbusres.2018.02.012 https://doi.org/10.1086/688165 https://doi.org/10.1108/09578231311325668 https://doi.org/10.1177/095207678700200102 https://doi.org/10.3102/0002831219872738 https://doi.org/10.1177/0042085919877932 https://doi.org/10.1080/10967490802494867 https://doi.org/10.1177/2332858416674901 Power, brokers, and agendas: New directions for the use of social network analysis in education policy 11 Howlett, M., Mukherjee, I., & Koppenjan, J. (2017). Policy learning and policy networks in theory and practice: The role of policy brokers in the Indonesian biodiesel policy network. Policy and Society, 36(2), 233–250. https://doi.org/10.1080/14494035.2017.1321230 Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136(April). https://doi.org/10.1016/j.tre.2020.101922 Kavi, A. (2020, July 22). Virus surge brings calls for Trump to invoke the Defense Production Act. The New York Times. https://www.nytimes.com/2020/07/22/us/politics/coronavirus- defense-production-act.html Kenis, P., & Schneider, V. (1991). Policy networks and policy analysis: scrutinizing a new analytical toolbox. In Policy networks: Empirical evidence and theoretical considerations (pp. 25–59). Campus Verlag. Laumann, E. O., Knoke, D., & Kim, Y. H. (1985). An organizational approach to state policy formation: a comparative study of energy and health domains. American Sociological Review, 50(1), 1–19. https://doi.org/10.2307/2095336 Liou, Y.-H. (2016). Tied to the Common Core: Exploring the characteristics of reform advice relationships of educational leaders. Educational Administration Quarterly, 52(5), 793–840. https://doi.org/10.1177/0013161X16664116 Lubienski, C. (2018). The critical challenge: Policy networks and market models for education. Policy Futures in Education, 16(2), 156–168. https://doi.org/10.1177/1478210317751275 Lubienski, C. (2019). Advocacy networks and market models for education. In Researching the global education industry (pp. 69–86). Palgrave Macmillan. https://doi.org/10.1007/978-3-030- 04236-3_4 Mainardes, J., & Gandin, L. A. (2013). Contributions of Stephen J. Ball to the research on educational and curriculum policies in Brazil. London Review of Education, 11(3), 256–264. https://doi.org/10.1080/14748460.2013.840985 Marcussen, M., & Olsen, H. P. (2007). Transcending analytical cliquishness with second-generation governance network analysis. In Democratic Network Governance in Europe (pp. 273–292). Palgrave Macmillan. https://doi.org/10.1057/9780230596283_14 Marsh, D. (1998). Comparing policy networks. Open University Press. Marsh, D., & Rhodes, R. A. W. (1992). Policy networks in British government. Clarendon Press.https://doi.org/10.1093/acprof:oso/9780198278528.001.0001 Miskel, C., & Song, M. (2004). Passing Reading First: Prominence and processes in an elite policy network. Educational Evaluation and Policy Analysis, 26(2), 89–109. https://doi.org/10.3102/01623737026002089 Moolenaar, N. M., Daly, A. J., & Sleegers, P. J. (2010). Occupying the principal position: Examining relationships between transformational leadership, social network position, and schools’ innovative climate. Educational Administration Quarterly, 46(5), 623–670. O’Toole Jr, L. J. (1997). Treating networks seriously: Practical and research-based agendas in public administration. Public Administration Review, 57(1), 45–52. https://doi.org/10.2307/976691 Paquin Morel, R. A. (2019). Test questions: Organizing, motivating, and mobilizing opposition to accountability testing. (Doctoral dissertation). Northwestern University, Evanston, IL. Regan, P. M., & Khwaja, E. T. (2019). Mapping the political economy of education technology: A networks perspective. Policy Futures in Education, 17(8), 1000–1023. https://doi.org/10.1177/1478210318819495 https://doi.org/10.1080/14494035.2017.1321230 https://doi.org/10.1016/j.tre.2020.101922 https://doi.org/10.2307/2095336 https://doi.org/10.1177/0013161X16664116 https://doi.org/10.1177/1478210317751275 https://doi.org/10.1007/978-3-030-04236-3_4 https://doi.org/10.1007/978-3-030-04236-3_4 https://doi.org/10.1080/14748460.2013.840985 https://doi.org/10.1057/9780230596283_14 https://doi.org/10.1093/acprof:oso/9780198278528.001.0001 https://doi.org/10.3102/01623737026002089 https://doi.org/10.2307/976691 https://doi.org/10.1177/1478210318819495 Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 12 Reckhow, S. (2013). Follow the money: How foundation dollars change public school politics. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199937738.001.0001 Reckhow, S., & Snyder, J. W. (2014). The expanding role of philanthropy in education politics. Educational Researcher, 43(4), 186–195. https://doi.org/10.3102/0013189X14536607 Reuters. (2020, July 27). SAP picked by Moderna to help distribute COVID-19 vaccine candidate. The New York Times. https://www.nytimes.com/reuters/2020/07/27/business/27reuters- sap-results-moderna.html Rhodes, R. A. (1990). Policy networks: A British perspective. Journal of Theoretical Politics, 2(3), 293– 317. https://doi.org/10.1177/0951692890002003003 Richardson, J. J., & Jordan, A. G. (1979). Governing under pressure. Martin Robertson. Rowan, B. (2002). The ecology of school improvement: Notes on the school improvement industry in the United States. Journal of Educational Change, 3(3–4), 283–314. https://doi.org/10.1023/A:1021277712833 Russell, J. L., Meredith, J., Childs, J., Stein, M. K., & Prine, D. W. (2014). Designing inter- organizational networks to implement education reform: An analysis of state race to the top applications. Educational Evaluation and Policy Analysis, 37(1), 92–112. https://doi.org/10.3102/0162373714527341 Saltman, K. (2014). The politics of education: A critical introduction. Paradigm Publishers. https://doi.org/10.4324/9781315632742 Schuster, J., Jörgens, H., & Kolleck, N. (2019). The rise of global policy networks in education: Analyzing Twitter debates on inclusive education using social network analysis. Journal of Education Policy, 1–21. https://doi.org/10.1080/02680939.2019.1664768 Scott, J., & Jabbar, H. (2014). The hub and the spokes: Foundations, intermediary organizations, incentivist reforms, and the politics of research evidence. Educational Policy, 28(2), 233–257. https://doi.org/10.1177/0895904813515327 Scott, J., Lubienski, C., DeBray, E., & Jabbar, H. (2014). The intermediary function in evidence production, promotion, and utilization: The case of educational incentives. In K. S. Finnigan & A. J. Daly (Eds.), Using research evidence in education: From the schoolhouse door to Capitol Hill (pp. 69–89). New York: Springer. https://doi.org/10.1007/978-3-319-04690-7_6 Song, M., & Miskel, C. G. (2005). Who are the influentials? A cross-state social network analysis of the reading policy domain. Educational Administration Quarterly, 41(1), 7–48. https://doi.org/10.1177/0013161X04269515 Song, M., & Miskel, C. G. (2007). Exploring the structural properties of the state reading policy domain using network visualization technique. Educational Policy, 21(4), 589–614. https://doi.org/10.1177/0895904806289264 Supovitz, J., Daly, A.J., del Fresno, M., & Kolouch, C. (2017). #commoncore Project. Retrieved from http://www.hashtagcommoncore.com. Wang, Y., & Fikis, D. J. (2019). Common Core State Standards on Twitter: Public sentiment and opinion leaders. Educational Policy, 33(4), 650–683. https://doi.org/10.1177/0895904817723739 Williamson, B. (2016). Political computational thinking: Policy networks, digital governance and ‘learning to code’. Critical Policy Studies, 10(1), 39–58. https://doi.org/10.1080/19460171.2015.1052003 https://doi.org/10.1093/acprof:oso/9780199937738.001.0001 https://doi.org/10.3102/0013189X14536607 https://doi.org/10.1177/0951692890002003003 https://doi.org/10.1023/A:1021277712833 https://doi.org/10.3102/0162373714527341 https://doi.org/10.4324/9781315632742 https://doi.org/10.1080/02680939.2019.1664768 https://doi.org/10.1177/0895904813515327 https://doi.org/10.1007/978-3-319-04690-7_6 https://doi.org/10.1177/0013161X04269515 https://doi.org/10.1177/0895904806289264 http://www.hashtagcommoncore.com/ https://doi.org/10.1177/0895904817723739 https://doi.org/10.1080/19460171.2015.1052003 Power, brokers, and agendas: New directions for the use of social network analysis in education policy 13 About the Authors/Guest Editors Emily M. Hodge Montclair State University hodgee@montclair.edu https://orcid.org/0000-0003-4165-8039 Emily M. Hodge, Ph.D., is an assistant professor in the Department of Educational Leadership at Montclair State University. She received her PhD from the Department of Education Policy Studies at the Pennsylvania State University. Her work uses qualitative methods and social network analysis to understand the changing nature of strategies for educational equity. Recent projects have explored how educational systems, schools, and teachers negotiate the tension between standardization and differentiation in the context of the Common Core State Standards, and the varied strategies state education agencies are using to support standards implementation. Joshua Childs The University of Texas at Austin joshuachilds@austin.utexas.edu Joshua Childs is an assistant professor of Educational Policy and Planning (EPP) in the Department of Educational Leadership and Policy. Joshua received his PhD in Learning Sciences and Policy at the University of Pittsburgh. Joshua's research focuses on the role of interorganizational networks, cross-sector collaborations, and strategic alliances to address complex educational issues. Specifically, his work examines collaborative approaches inv olving community organizations and stakeholders that have the potential to improve academic achievement and reduce opportunity gaps for students in urban and rural schools. Wayne Au University of Washington, Bothell wayneau@uw.edu Wayne Au is an educator, activist, and scholar who focuses on issues of race, class, and power in schooling. He is a professor in the School of Educational Studies at the University of Washington-Bothell, where he currently serves as dean of diversity and equity. Au is an editor of the social justice teacher magazine Rethinking Schools and the author or editor of numerous other publications, including Teaching for Black Lives, Rethinking Ethnic Studies, and A Marxist Education: Learning to Change the World. mailto:hodgee@montclair.edu https://orcid.org/0000-0003-4165-8039 mailto:joshuachilds@austin.utexas.edu mailto:wayneau@uw.edu Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 14 SPECIAL ISSUE Researching 21st Century Education Policy Through Social Network Analysis education policy analysis archives Volume 28 Number 117 August 17, 2020 ISSN 1068-2341 Readers are free to copy, display, distribute, and adapt this article, as long as the work is attributed to the author(s) and Education Policy Analysis Archives, the changes are identified, and the same license applies to the derivative work. More details of this Creative Commons license are available at https://creativecommons.org/licenses/by-sa/4.0/. EPAA is published by the Mary Lou Fulton Institute and Graduate School of Education at Arizona State University Articles are indexed in CIRC (Clasificación Integrada de Revistas Científicas, Spain), DIALNET (Spain), Directory of Open Access Journals, EBSCO Education Research Complete, ERIC, Education Full Text (H.W. Wilson), QUALIS A1 (Brazil), SCImago Journal Rank, SCOPUS, SOCOLAR (China). Please send errata notes to Audrey Amrein-Beardsley at audrey.beardsley@asu.edu Join EPAA’s Facebook community at https://www.facebook.com/EPAAAAPE and Twitter feed @epaa_aape. https://creativecommons.org/licenses/by-sa/4.0/ http://www.doaj.org/ mailto:audrey.beardsley@asu.edu https://www.facebook.com/EPAAAAPE Power, brokers, and agendas: New directions for the use of social network analysis in education policy 15 education policy analysis archives editorial board Lead Editor: Audrey Amrein-Beardsley (Arizona State University) Editor Consultor: Gustavo E. Fischman (Arizona State University) Associate Editors: Melanie Bertrand, David Carlson, Lauren Harris, Danah Henriksen, Eugene Judson, Mirka Koro-Ljungberg, Daniel Liou, Scott Marley, Molly Ott, Iveta Silova (Arizona State University) Madelaine Adelman Arizona State University Amy Garrett Dikkers University of North Carolina, Wilmington Gloria M. Rodriguez University of California, Davis Cristina Alfaro San Diego State University Gene V Glass Arizona State University R. Anthony Rolle University of Houston Gary Anderson New York University Ronald Glass University of California, Santa Cruz A. G. Rud Washington State University Michael W. Apple University of Wisconsin, Madison Jacob P. K. Gross University of Louisville Patricia Sánchez University of University of Texas, San Antonio Jeff Bale University of Toronto, Canada Eric M. Haas WestEd Janelle Scott University of California, Berkeley Aaron Benavot SUNY Albany Julian Vasquez Heilig California State University, Sacramento Jack Schneider University of Massachusetts Lowell David C. Berliner Arizona State University Henry Braun Boston College Kimberly Kappler Hewitt University of North Carolina Greensboro Noah Sobe Loyola University Casey Cobb University of Connecticut Aimee Howley Ohio University Nelly P. Stromquist University of Maryland Arnold Danzig San Jose State University Steve Klees University of Maryland Jaekyung Lee SUNY Buffalo Benjamin Superfine University of Illinois, Chicago Linda Darling-Hammond Stanford University Jessica Nina Lester Indiana University Adai Tefera Virginia Commonwealth University Elizabeth H. DeBray University of Georgia Amanda E. Lewis University of Illinois, Chicago A. Chris Torres Michigan State University David E. DeMatthews University of Texas at Austin Chad R. Lochmiller Indiana University Tina Trujillo University of California, Berkeley Chad d'Entremont Rennie Center for Education Research & Policy Christopher Lubienski Indiana University Federico R. Waitoller University of Illinois, Chicago John Diamond University of Wisconsin, Madison Sarah Lubienski Indiana University Larisa Warhol University of Connecticut Matthew Di Carlo Albert Shanker Institute William J. Mathis University of Colorado, Boulder John Weathers University of Colorado, Colorado Springs Sherman Dorn Arizona State University Michele S. Moses University of Colorado, Boulder Kevin Welner University of Colorado, Boulder Michael J. Dumas University of California, Berkeley Julianne Moss Deakin University, Australia Terrence G. Wiley Center for Applied Linguistics Kathy Escamilla University of Colorado, Boulder Sharon Nichols University of Texas, San Antonio John Willinsky Stanford University Yariv Feniger Ben-Gurion University of the Negev Eric Parsons University of Missouri-Columbia Jennifer R. Wolgemuth University of South Florida Melissa Lynn Freeman Adams State College Amanda U. Potterton University of Kentucky Kyo Yamashiro Claremont Graduate University Rachael Gabriel University of Connecticut Susan L. Robertson Bristol University Miri Yemini Tel Aviv University, Israel Education Policy Analysis Archives Vol. 2 8 No. 117 SPECIAL ISSUE 16 archivos analíticos de políticas educativas consejo editorial Editor Consultor: Gustavo E. Fischman (Arizona State University) Editores Asociados: Felicitas Acosta (Universidad Nacional de General Sarmiento), Armando Alcántara Santuario (Universidad Nacional Autónoma de México), Ignacio Barrenechea, Jason Beech ( Universidad de San Andrés), Angelica Buendia, (Metropolitan Autonomous University), Alejandra Falabella (Universidad Alberto Hurtado, Chile), Veronica Gottau (Universidad Torcuato Di Tella), Carolina Guzmán-Valenzuela (Universidade de Chile), Antonio Luzon, (Universidad de Granada), Tiburcio Moreno (Autonomous Metropolitan University-Cuajimalpa Unit), José Luis Ramírez, (Universidad de Sonora), Axel Rivas (Universidad de San Andrés), Maria Veronica Santelices (Pontificia Universidad Católica de Chile) Claudio Almonacid Universidad Metropolitana de Ciencias de la Educación, Chile Ana María García de Fanelli Centro de Estudios de Estado y Sociedad (CEDES) CONICET, Argentina Miriam Rodríguez Vargas Universidad Autónoma de Tamaulipas, México Miguel Ángel Arias Ortega Universidad Autónoma de la Ciudad de México Juan Carlos González Faraco Universidad de Huelva, España José Gregorio Rodríguez Universidad Nacional de Colombia, Colombia Xavier Besalú Costa Universitat de Girona, España María Clemente Linuesa Universidad de Salamanca, España Mario Rueda Beltrán Instituto de Investigaciones sobre la Universidad y la Educación, UNAM, México Xavier Bonal Sarro Universidad Autónoma de Barcelona, España Jaume Martínez Bonafé Universitat de València, España José Luis San Fabián Maroto Universidad de Oviedo, España Antonio Bolívar Boitia Universidad de Granada, España Alejandro Márquez Jiménez Instituto de Investigaciones sobre la Universidad y la Educación, UNAM, México Jurjo Torres Santomé, Universidad de la Coruña, España José Joaquín Brunner Universidad Diego Portales, Chile María Guadalupe Olivier Tellez, Universidad Pedagógica Nacional, México Yengny Marisol Silva Laya Universidad Iberoamericana, México Damián Canales Sánchez Instituto Nacional para la Evaluación de la Educación, México Miguel Pereyra Universidad de Granada, España Ernesto Treviño Ronzón Universidad Veracruzana, México Gabriela de la Cruz Flores Universidad Nacional Autónoma de México Mónica Pini Universidad Nacional de San Martín, Argentina Ernesto Treviño Villarreal Universidad Diego Portales Santiago, Chile Marco Antonio Delgado Fuentes Universidad Iberoamericana, México Omar Orlando Pulido Chaves Instituto para la Investigación Educativa y el Desarrollo Pedagógico (IDEP) Antoni Verger Planells Universidad Autónoma de Barcelona, España Inés Dussel, DIE-CINVESTAV, México José Ignacio Rivas Flores Universidad de Málaga, España Catalina Wainerman Universidad de San Andrés, Argentina Pedro Flores Crespo Universidad Iberoamericana, México Juan Carlos Yáñez Velazco Universidad de Colima, México javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/819') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/820') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/4276') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/1609') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/825') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/797') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/823') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/798') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/555') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/814') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/2703') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/801') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/826') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/802') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/3264') javascript:openRTWindow('http://epaa.asu.edu/ojs/about/editorialTeamBio/804') Power, brokers, and agendas: New directions for the use of social network analysis in education policy 17 arquivos analíticos de políticas educativas conselho editorial Editor Consultor: Gustavo E. Fischman (Arizona State University) Editoras Associadas: Andréa Barbosa Gouveia (Universidade Federal do Paraná), Kaizo Iwakami Beltrao, (Brazilian School of Public and Private Management - EBAPE/FGVl), Sheizi Calheira de Freitas (Federal University of Bahia), Maria Margarida Machado, (Federal University of Goiás / Universidade Federal de Goiás), Gilberto José Miranda, (Universidade Federal de Uberlândia, Brazil), Marcia Pletsch, Sandra Regina Sales (Universidade Federal Rural do Rio de Janeiro) Almerindo Afonso Universidade do Minho Portugal Alexandre Fernandez Vaz Universidade Federal de Santa Catarina, Brasil José Augusto Pacheco Universidade do Minho, Portugal Rosanna Maria Barros Sá Universidade do Algarve Portugal Regina Célia Linhares Hostins Universidade do Vale do Itajaí, Brasil Jane Paiva Universidade do Estado do Rio de Janeiro, Brasil Maria Helena Bonilla Universidade Federal da Bahia Brasil Alfredo Macedo Gomes Universidade Federal de Pernambuco Brasil Paulo Alberto Santos Vieira Universidade do Estado de Mato Grosso, Brasil Rosa Maria Bueno Fischer Universidade Federal do Rio Grande do Sul, Brasil Jefferson Mainardes Universidade Estadual de Ponta Grossa, Brasil Fabiany de Cássia Tavares Silva Universidade Federal do Mato Grosso do Sul, Brasil Alice Casimiro Lopes Universidade do Estado do Rio de Janeiro, Brasil Jader Janer Moreira Lopes Universidade Federal Fluminense e Universidade Federal de Juiz de Fora, Brasil António Teodoro Universidade Lusófona Portugal Suzana Feldens Schwertner Centro Universitário Univates Brasil Debora Nunes Universidade Federal do Rio Grande do Norte, Brasil Lílian do Valle Universidade do Estado do Rio de Janeiro, Brasil Geovana Mendonça Lunardi Mendes Universidade do Estado de Santa Catarina Alda Junqueira Marin Pontifícia Universidade Católica de São Paulo, Brasil Alfredo Veiga-Neto Universidade Federal do Rio Grande do Sul, Brasil Flávia Miller Naethe Motta Universidade Federal Rural do Rio de Janeiro, Brasil Dalila Andrade Oliveira Universidade Federal de Minas Gerais, Brasil