Institute of Politics - Blueprint for Action
Methodology:
1. Internal Interviews: Harvard IOP Student Leadership
2. External Interviews: IOP Student & Staff Leadership at Other Select Universities
3. Network Survey: Harvard IOP Student & Staff Leadership
METHOD 1: Internal Interviews
Given that we received no interest from staff to participate in our interview process, we held individual meetings with seven members of our Student Advisory Committee. In an effort to understand the broad landscape of communication at the IOP, these student leaders are drawn from a variety of roles and programs. Our interviewees range to those from old and young programs, those from programs with dedicated full-time staff members and those who report to the Director of Student Programs (who is meant to oversee over half of our programs), a member of the Executive Team, leaders from all class years, as well as those who have participated in SAC before and those who are experiencing their first leadership cycle. In hosting these discussions, we allowed students to articulate their feelings of connection to other corners of the Institute, provide insights into potential initiatives to increase collaboration across student leaders, and inform our recommendations for enhanced communication at the IOP.
As a general outline, we inquired the following from our student leadership:
From these discussions, we generated the following findings and recommendations regarding communication and collaboration networks at the IOP:
METHOD 2: External Interviews
In order to ground our recommendations in an understanding of other potential mechanisms for communication and collaboration structures, we interviewed student and staff leadership at five of our counterpart Institute of Politics organizations at universities across the country. Through this process, we learned of the communication networks and relations between students and staff at the New Hampshire Institute of Politics at Saint Anselm College, the Institute of Politics and Public Service at Georgetown University, the University of Chicago Institute of Politics, the USC Center for the Political Future, and the Eagleton Institute of Politics at Rutgers University.
As a general outline, we inquired the following from these external leaders:
From these conversations, we generated the following findings and recommendations:
METHOD 3: Network Survey
In order to create a concrete visualization of the state of communication between and amongst students and staff at the IOP, we conducted a survey of our SAC and staff members, which allowed us to formulate a social network model based on frequency of communication. Ironically, we were unable to garner participation from a majority of our staff members, although almost every single student leader completed it. Although this makes our model technically inaccurate, there are still many valuable insights that we can draw from the final model when taking into account the incomplete dataset.
Within our survey, we asked each respondent to state their name and role in the IOP, then proceed through a list of every student leader and staff member and rank the frequency of their communication with those individuals on a weekly basis on the following scale of 0-3:
Findings
Weighted by Degree
Degree measures is the number of edges on a node (object/person of interest), or how many other nodes a node is connected to. For example, Janna has a degree of 43, which means she’s connected to 43 other members of the IOP, which is why she’s also the largest and darkest colored node. Ethan, on the other hand, has a degree of 3, which is why he’s one of the lightest colored and smallest nodes. This filter helps us identify who the key players in the IOP are, and who are most likely to serve as bridges between subgroups, and who have potential to be at the heart of those subgroups, like Stephane, who is a medium sized/colored node in the top left.Divided by Modularity Class
Modularity detects subgroups within the network, typically characterized by dense connections amongst themselves and sparse connections with nodes of different subgroups. The IOP is not as segregated as many other networks, such as large companies, where there is very little communication between departments. However, running a modularity test still uncovered 4 different subgroups. The pink group, comprising 38.3% of the network is composed almost entirely of students, save for Abbie, the Director of Student Programs. The second largest subgroup in green, comprising 34.04% of the network, is almost completely comprised of staff members, except for three students who are leaders of programs that have full-time paid staff members specifically for their programs. The next largest subgroup, in orange and comprising 23.4% of the network is a secondary student network, again with a singular staff member, Setti. By looking at the model we can see that the two student subgroups are actually quite integrated, however, together they are too large to be considered a subgroup, and, when combined with underrepresented staff incorporating into the mix, it makes sense why the algorithm arbitrarily divided them. The final subgroup, in blue and comprising 4.26% of the network, is composed of the 2 co-leaders of the conferences committee, indicating that they are likely quite isolated from the rest of the IOP as a whole. It should be noted that the two staff members in the student groups did not respond to the survey, so their connections to other staff members are underrepresented and would likely be closer connected, and perhaps even integrated into the staff subgroup if they had responded. The model clearly demonstrates that staff and students are divided into separate subgroups.
Weighted by Betweenness Centrality
Betweenness Centrality is a tool typically used to identify bridges in a network since nodes with a high betweenness centrality tend to serve as a conduit for a higher number of other nodes. In the model, the main bridges identified are Janna, Tabitha, Kerri, and, to an extent, Amen. Since the positions they hold are that of President, Vice President, Receptionist/Staff Assistant, and Communications Director, respectively, this is unsurprising and reinforces our hypothesis that the executive board of the IOP would be the primary conduits between students and staff. Although it is certainly good that the primary leaders of the IOP are fulfilling their duties, it is certainly somewhat concerning that almost no other members of the IOP have a betweenness centrality nearly as high as them, with staff member Stephane being the only other node coming close. This means that, at least in terms of communication amongst leadership, the IOP is a highly centralized organization, and there are almost no cases in which linkage between lower levels of leadership is stronger than with the higher levels of leadership.
Network Conclusions
The communication network model of the IOP, despite being incomplete, provides us with concrete quantitative evidence that there exists a divide between staff and the majority of student leaders, except for the core executive team and the few programs that work closely with individual staff members. Furthermore, the lack of responsiveness from staff to our survey is even further evidence of the divide, and if anything primed the network to misrepresent the IOP as more unified, meaning the true network is likely even more divided.
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