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Concrete Diversity Initiatives in Political Science: A Faculty Workload Intervention Program

Published online by Cambridge University Press:  05 October 2022

Heather Stoll
Affiliation:
University of California, Santa Barbara, USA
Michele McLaughlin-Zamora
Affiliation:
University of California, Santa Barbara, USA
Sarah E. Anderson
Affiliation:
University of California, Santa Barbara, USA
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Abstract

Type
Structuring Inclusion into Political Science Recruiting, Progression, and Engagement
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the American Political Science Association

Recent events, including the COVID-19 pandemic, racial justice protests, and the #MeToo Movement, highlighted the various ways that systemic racism and sexism persist in academia. Underrepresentation, obstacles to career advancement, and difficult department climates persist for women and underrepresented minority (URM) faculty, despite what we know now about this “leaky pipeline” (American Political Science Association 2004).

Political science is no exception. In 2020, the American Political Science Association (APSA) renewed its commitment to diversity, equity, and inclusion (DEI) (American Political Science Association 2020). However, as discussed in Reinhardt and King’s introduction to this symposium, the political science profession remains one of the most male- and white-dominated social science fields (Mershon and Walsh Reference Mershon and Walsh2016; Michelson and Monforti Reference Michelson and Monforti2021).

Inequities in faculty workloads are one principal source of the leaky faculty pipeline. Vital to the functioning of institutions of higher education, service and mentoring are both major elements of faculty workloads. However, these work activities have been shown to be disproportionately undertaken by women and historically marginalized groups. Although the literature currently does not offer a satisfactory explanation for these observed workload inequities, it is unambiguous about the negative consequences for a host of outcomes, ranging from department climate to retention to advancement through the ranks of the professoriate.

Unfortunately, and complicating APSA’s mission to address systemic inequity, few programs exist that attempt evidence-based interventions. To move the disciplinary conversation forward and to add a tool to the resources of political science departments, this article describes a faculty workload intervention program based on O’Meara et al.’s (Reference O’Meara, Jaeger, Misra, Lennartz and Kuvaeva2018) Faculty Workloads and Rewards Project (FWRP) that also includes the work activity area of mentoring. Here, we apply this program to a hypothetical political science department. This workload intervention program currently is being implemented at four pilot departments across the University of California, Santa Barbara (UCSB) campus, with funding from the University of California Office of the President’s Advancing Faculty Diversity program.

THE PROBLEM

National-level research on academia consistently finds significant differences in faculty workloads by gender and race/ethnicity (Guarino and Borden Reference Guarino and Victor2017; Hurtado et al. Reference Hurtado, Eagan, Pryor, Wang and Tran2012; O’Meara, Kuvaeva, and Nyunt Reference O’Meara, Kuvaeva and Nyunt2017; Wood, Hilton, and Nevarez Reference Wood, Hilton and Nevarez2015). Specifically, evidence shows that women faculty typically spend more time than men faculty on teaching and service activities and less time on research (Misra et al. Reference Misra, Lundquist, Holmes and Agiomavritis2011). Similarly, URM faculty spend more time on service, teaching, and mentoring than non-URM faculty. Furthermore, women of color—faculty members at the intersection of these two identity categories—face particularly onerous service demands (Hurtado and Figueroa Reference Hurtado and Figueroa2013).

The academic literature suggests that similar dynamics are active in the political science discipline. For example, in their analysis of the 2009 APSA faculty survey, Mitchell and Hesli (Reference Mitchell and Hesli2013) found that women faculty in political science supervise more advisees and contribute more service to their department and/or college than men faculty. Additionally, the service performed by women often is less prestigious and does not contribute to their career advancement.

A particularly critical form of service in which women and minority faculty engage is the mentoring of fellow faculty members and students. This service is valued by these faculty members, and it has a key role in the career progression of faculty from traditionally underrepresented groups. For example, the APSA Committee on the Status of Blacks (Alex-Assenshoh et al. Reference Alex-Assenshoh, Givens, Golden, Hutchings, Wallace and Whitby2004) found that a key element of career satisfaction and retention was the mentoring relationship between junior and senior faculty members of the same ethnoracial group.

These workload inequities affect climate (e.g., perceptions of fairness and equity, satisfaction with teaching and service workloads, sense of belonging, and stress), which in turn affects performance, advancement, and retention (Eagan and Garvey Reference Eagan and Garvey2015; Hart and Cress Reference Hart and Cress2008; O’Meara et al. Reference O’Meara, Lennartz, Kuvaeva, Jaeger and Misra2019). With service and mentoring work often either not counted or devalued in academic reward systems (e.g., tenure, promotion, and salary), systemic inequities in workloads also directly contribute to lower tenure and promotion rates, longer time to advancement, and greater career dissatisfaction of women and minority faculty (Fox and Colatrella Reference Fox and Colatrella2006; O’Meara et al. Reference O’Meara, Jaeger, Misra, Lennartz and Kuvaeva2018).

In political science, workload inequities contribute to lower levels of collegiality, productivity, research, and retention of diverse faculty, especially women of color, while also undermining the profession’s goal of advancing knowledge through diverse and inclusive research agendas (Mershon and Walsh Reference Mershon and Walsh2016). The upward trend of women of color who are entering the field will require a more inclusive climate to remain in the profession (Michelson and Monforti Reference Michelson and Monforti2021).

In political science, workload inequities contribute to lower levels of collegiality, productivity, research, and retention of diverse faculty, especially faculty women of color, while also undermining the profession’s goal of advancing knowledge through diverse and inclusive research agendas.

THE GOAL: INCREASING FACULTY DIVERSITY AND INCLUSION WITH EQUITY-MINDED WORKLOAD REFORM

With funding from the National Science Foundation Advance (IHE PLAN Award No. 1463898), O’Meara et al. (Reference O’Meara, Jaeger, Misra, Lennartz and Kuvaeva2018) developed the FWRP to implement a program of “equity-minded faculty workload reform” (O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021).Footnote 1 Equity-minded workloads such as this project seek to advance transparency, clarity, credit, norms, context, and accountability (O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021). Aligned with APSA’s goals, the aim is to address practices that contribute to workload inequities involving race and gender. The intervention included a workshop on how implicit bias can shape faculty workloads; the collection and sharing of faculty work-activity data (i.e., a “dashboard”); use of the dashboard to identify equity issues; and department development of an “equity action plan” including the adoption of organizational practices aimed at solving any equity issues revealed by the dashboard. Faculty members also had the option of participating in an individual time-management and planning webinar.

Equity-minded workloads such as this project seek to advance transparency, clarity, credit, norms, context, and accountability.

The randomized control trial, conducted over 18 months in approximately 30 departments in four-year universities in Maryland, Massachusetts, and North Carolina, provides strong support for the intervention strategies. Specifically, the FWRP was shown to improve transparency in the work that faculty are doing; enhance clarity in roles and expectations; increase faculty job satisfaction and perceptions of fairness; and reduce intent to leave. For example, faculty in participating departments were significantly more likely than those in control departments to report that the distribution of teaching and service work was fair (O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021).

IMPLEMENTING AN EQUITY-MINDED WORKLOAD REFORM PROGRAM IN A POLITICAL SCIENCE DEPARTMENT

This section describes a hypothetical political science department faculty workload intervention program based on the FWRP and drawn from the one being piloted at UCSB. Our program launched in Summer 2020 and was ongoing at the time of writing through Summer 2022, so we cannot yet report specific outcomes. Nevertheless, we hope it serves as a useful model for political science departments interested in addressing faculty workload inequities.

Our hypothetical program focused on what we considered key components of the FWRP intervention (O’Meara et al. Reference O’Meara, Jaeger, Misra, Lennartz and Kuvaeva2018): the departmental “choice architecture” (O’Meara et al. Reference O’Meara, Jaeger, Misra, Lennartz and Kuvaeva2018; O’Meara et al. Reference O’Meara, Lennartz, Kuvaeva, Jaeger and Misra2019) and transparent workload data to guide decision making. Moreover, this program encompasses service, teaching, and mentoring work activities. We suggest elevating mentoring work activities, which were not a focus of the FWRP, to a level on par with service and teaching because of their importance to women and URM faculty in political science. Mentoring workloads are the most invisible but can be made visible via the program.

As part of the program, a department develops the following four products:

  1. 1. A dashboard for collecting and disseminating faculty workload activity data.

  2. 2. A workload credit schema for assigning credit to work activities of different time intensities.

  3. 3. Expected workloads (standards) by rank.

  4. 4. Organizational practices for addressing equity issues, such as a system for assigning service roles.

Because studies that reported on the FWRP describe the products (O’Meara et al. 2020; Reference O’Meara, Culpepper, Misra and Jaeger2021), our focus in this article is on providing examples and highlighting key issues in the political science context. The online appendix provides a sample dashboard template with a credit schema and expected workloads (i.e., Products #1–3), which uses a point system. Examples drawn from this template inform the following discussion. An undergraduate-only department will need to modify these outcomes—for example, by removing graduate student mentoring from the dashboard.

Faculty Work Activity Dashboards

A work activity dashboard is “an easy-to-read display of faculty work areas across different work activities,” usually in the form of a simple table or chart (O’Meara et al. 2020, 35) that is shared with faculty members. This enables them to more clearly understand their colleagues’ workloads by promoting transparency and making inequities apparent.

Tables 1–3 provide basic examples in the form of excerpts drawn from the template: a service dashboard (see table 1); a teaching dashboard (see table 2); and a mentoring dashboard (see table 3). Data are shown for three hypothetical faculty members: Bob, Hilaria, and Jamal. Two of these faculty members are URMs (Hilaria and Jamal) and one is a woman (Hilaria).

Table 1 Example of a Service Dashboard (Excerpts)

Table 2 Example of a Teaching Dashboard (Excerpts)

Table 3 Example of a Mentoring Dashboard (Excerpts)

The first major operational decision that departments must make is which work activities to include in the dashboards. Examples are in tables 1–3, with additional examples in the template (see the online appendix). The scope of each set of activities, especially mentoring, is likely to require deliberation by the faculty.

A second major operational decision is about transparency. For example, in tables 1–3, information about work activities is reported at the individual faculty level with names attached. An alternative is to provide anonymity by replacing names with an identifier (e.g., F-2) or to report only at the aggregate level (e.g., averages by rank, gender, and race).

Workload Credit Schemes

Workload credit schemes build on the dashboard by assigning differential credit to faculty members for work activities of different intensities and effort levels. This allows for faculty time to be distributed equitably and contributions to be valued appropriately. However, it may require time for a department to reach consensus about how to credit many of the activities.

One approach is to assign more points to work activities that require more time and effort, as illustrated in tables 1–3. For example, in our hypothetical political science department’s service work activities, serving on the website committee and as the comparative politics subfield convener are viewed as low intensity because they require only a few hours of work per quarter or semester. These activities are assigned 1 point. Conversely, high-intensity positions, such as graduate admissions or a search committee, are those that require many hours of work. These activities are assigned 3 points. Similarly, chairing a committee or assuming another leadership role requires more work than simply serving on a committee. Accordingly, those who fill leadership roles are awarded additional points, such as 1 point for chairing a high-intensity committee.

Another example is a mentoring relationship that entails frequent meetings over the course of an academic year and the reading of students’ work (e.g., serving on a dissertation committee). Such high-intensity mentoring is awarded more points by our hypothetical department than low-intensity mentoring, which requires only a few meetings per year (e.g., one to two meetings with an undergraduate to discuss career paths). Medium-intensity mentoring, such as mentoring a junior faculty member at another university through the APSA Mentor Program, which involves three to four meetings per semester or quarter, is awarded an intermediate number of points.

In a final example, teaching a large, intensive, lower-division introductory course (e.g., American politics or international relations) would be viewed by many faculty as a higher-intensity teaching activity than teaching a small, elective, upper-division course (e.g., a 15-student seminar on political parties). To reflect this, our hypothetical department awards an additional point for large-enrollment classes. It also awards an additional half point for intensive service classes that entail substantial student contact and require frequent feedback on student work (e.g., political methodology courses).

Point systems are not the only way to award differential credit; other systems are possible. For example, departments simply might classify activities as “regular/minor” versus “above normal effort/major.” Alternatively, they may require faculty to track the hours expended.

An additional consideration that may not be captured by official records of time spent is the emotional labor disproportionately carried out by women and URM faculty. This effort may be especially high intensity at times, such as when helping a colleague deal with harassment. Deciding how to count this effort also likely will require deliberation by a department; again, there are different options. For example, with a point system, a faculty member engaged in this emotional labor might be awarded an “extra-effort” point. Alternatively, this labor might provide justification for a higher evaluation in the personnel process than otherwise would be given on the basis of quantitative metrics alone.

Departmental Standards by Rank

After weighing work activities by their time intensity, departmental standards articulate what the expected service, teaching, and mentoring workloads are for faculty of different ranks. When standards are clear, objective, and equally applied, biases are reduced, perceptions of equity and fairness are enhanced, and more equitable workloads result. The right-hand columns in tables 1–3 provide examples of point-based departmental standards for service, mentoring, and teaching workloads. In this example, the service and mentoring workload standards differ by rank but the teaching workload standards do not.

Equity Analysis and Remedies through Organizational Practices

After data on faculty work activities, weighed by intensity, are collected and shared in the dashboard, an analysis is undertaken and the department develops organizational policies and practices to address any equity issues identified (i.e., Product #4). Workloads are compared across rank, gender, and race as well as departmental standards. The department can commit to either (1) rewarding overperformers—like Jamal and Hilaria in our hypothetical department—as part of the personnel (merit) process; or (2) compensating them in other ways, such as by developing policies for work activity swaps in the future (O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021). Evidence of inequities in service and teaching workloads around race and gender also suggests that how assignments are made should be reconsidered. For example, our department might work on building into its service-assignment scheme opt-out instead of opt-in elements, as well as rotations of particularly intensive positions (O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021).

UNDOING THE “CAN OF WORMS”

When the FWRP began, a common warning was to be careful not to “open that can of worms,” meaning that either reform was not needed or attempts would only create more tension (O’Meara Reference O’Meara2018; O’Meara et al. Reference O’Meara, Culpepper, Misra and Jaeger2021). Although many FWRP pilot departments reported positive experiences, negative experiences and frustration were reported by some. However, as O’Meara (Reference O’Meara2018) argued, the can of worms is already open; inequities exist that are known to have negative consequences for individual faculty members, minoritized groups, and the discipline at large. Doing nothing has real costs for political science. A workload intervention program such as the one described in this article can make a positive difference.

However, more research is needed about how to best ensure that change is sustained in the long term. For example, how important is the leadership role played by the chair, and to what extent does a department’s efforts need support from the dean or broader university administration? Based on our experiences, we are of two minds. We can point to departments that have implemented a program like this one and sustained it over a number of years, solely through the commitment of individual faculty members and departmental leadership. We also see support from the administration as facilitating large-scale and long-lasting change, especially in departments in which the faculty initially is less receptive. Certainly, higher-education administrators can and should have a role in promoting social justice and equity (Kezar and Posselt Reference Kezar and Posselt2020). We look forward to more research on these matters, which will be facilitated by more departments experimenting with interventions to “undo the can of worms.”

CONCLUSION

Although addressing unequal faculty workloads may result in short-term discomfort, the long-term potential as a concrete diversity initiative is significant. Faculty workload intervention programs have the capacity to meet the goals of advancing DEI within the political science profession in visible, measurable ways. This article illustrates one such program for a hypothetical political science department. Using this program, departments can take action to create a more equity-minded workplace, enhancing the climate for and retention of women and historically minoritized groups.

Faculty workload intervention programs have the capacity to meet the goals of advancing DEI within the political science profession in visible, measurable ways.

ACKNOWLEDGMENTS

The authors are three members of a five-person team bringing the workload intervention program to UCSB. We gratefully acknowledge the work of our colleagues and co-PIs, Elizabeth Belding and Aida Hurtado. We also gratefully acknowledge project sponsors at UCSB, including Executive Vice Chancellor, David Marshall, and all of the academic deans. We appreciate the funding support from the University of California Office of the President and Dean of Social Sciences, Charlie Hale.

Supplementary Materials

To view supplementary material for this article, please visit http://doi.org/10.1017/S1049096522000877.

CONFLICTS OF INTEREST

The authors declare that there are no ethical issues or conflicts of interest in this research.

Footnotes

1. The six conditions they identify that are linked to equity-minded workloads and that guide equity-minded reforms are transparency, clarity, credit, norms, context, and accountability.

References

REFERENCES

Alex-Assenshoh, Yvette M., Givens, Terri, Golden, Kathie, Hutchings, Vincent L., Wallace, Sherri L., and Whitby, Kenny J.. 2004. “Mentoring and African American Political Scientists.” PSOnline. www.apsanet.org/statuscommitteeblacks.Google Scholar
American Political Science Association. 2004. Women’s Advancement in Political Science: A Report on the APSA Workshop on the Advancement of Women in Academic Political Science in the United States. Washington, DC: American Political Science Association. www.apsanet.org/portals/54/Files/Task%20Force%20Reports/Womens_Advancement_in_Political_Science_2005.pdf.Google Scholar
American Political Science Association. 2020. 2020 APSA Presidential Address. Washington, DC: American Political Science Association. www.apsanet.org/divresources.Google Scholar
Eagan, M. Kevin Jr. and Garvey, Jason C.. 2015. “Stressing Out: Connecting Race, Gender, and Stress with Faculty Productivity.” Journal of Higher Education 86 (6): 923–54.CrossRefGoogle Scholar
Fox, Mary Frank, and Colatrella, Carol. 2006. “Participation, Performance, and Advancement of Women in Academic Science and Engineering: What Is at Issue and Why.” Journal of Technology Transfer 31 (3): 377–86.CrossRefGoogle Scholar
Guarino, Cassandra M., and Victor, M. H. Borden. 2017. “Faculty Service Loads and Gender: Are Women Taking Care of the Academic Family?Research in Higher Education 58 (6): 672–94.CrossRefGoogle Scholar
Hart, Jennifer L., and Cress, Christine M.. 2008. “Are Women Faculty Just ‘Worrywarts’? Accounting for Gender Differences in Self-Reported Stress.” Journal of Human Behavior in the Social Environment 17 (1–2): 175–93.CrossRefGoogle Scholar
Hurtado, Sylvia, Eagan, Kevin, Pryor, John H., Wang, Hannah, and Tran, Serge. 2012. Undergraduate Teaching Faculty: The 2010–2011 HERI Faculty Survey. Los Angeles: Higher Education Research Institute.Google Scholar
Hurtado, Sylvia, and Figueroa, Tanya. 2013. “Women of Color Faculty in Science, Technology, Engineering, and Mathematics (STEM): Experiences in Academia.” Paper presented at the American Educational Research Association. San Francisco, April.Google Scholar
Kezar, Adrianna, and Posselt, Julie (eds.). 2020. Higher Education Administration for Social Justice and Equity. Oxfordshire, UK: Routledge Press.Google Scholar
Mershon, Carol, and Walsh, Denise. 2016. “Diversity in Political Science: Why It Matters and How to Get It.” Politics, Groups, and Identities 4 (3): 462–66.CrossRefGoogle Scholar
Michelson, Melissa R., and Monforti, Jessica L. Lavariega. 2021. “Elusive Inclusion: Persistent Challenges Facing Women of Color in Political Science.” PS: Political Science & Politics 54 (1): 152–57.Google Scholar
Misra, Joya, Lundquist, Jennifer Hickes, Holmes, Elissa, and Agiomavritis, Stephanie. 2011. “The Ivory Ceiling of Service Work.” Academe 97:26.Google Scholar
Mitchell, Sara McLaughlin, and Hesli, Vicki L.. 2013. “Women Don’t Ask? Women Don’t Say No? Bargaining and Service in the Political Science Profession.” PS: Political Science & Politics 46 (2): 355–69.Google Scholar
O’Meara, KerryAnn. 2018. “Undoing the Can of Worms.” Inside Higher Ed, June 27.Google Scholar
KerryAnn, O’Meara, Beise, Elizabeth, Culpepper, Dawn, Misra, Joya, and Jaeger, Audrey. 2020. “Faculty Work Activity Dashboards: A Strategy to Increase Transparency.” Change: The Magazine of Higher Learning 52 (3): 3442.Google Scholar
O’Meara, KerryAnn, Culpepper, Dawn, Misra, Joya, and Jaeger, Audrey. 2021. “Equity-Minded Faculty Workloads: What We Can and Should Do Now.” Washington, DC: American Council on Education. www.acenet.edu/Documents/Equity-Minded-Faculty-Workloads.pdf.Google Scholar
O’Meara, KerryAnn, Jaeger, Audrey, Misra, Joya, Lennartz, Courtney Jo, and Kuvaeva, Alexandra. 2018. “Undoing Disparities in Faculty Workloads: A Randomized Trial Experiment.” PLoS ONE 13 (12): e0207316. https://doi.org/10.1371/journal.pone.0207316.CrossRefGoogle Scholar
O’Meara, KerryAnn, Kuvaeva, Alexandra, and Nyunt, Gudrun. 2017. “Constrained Choices: A View of Campus Service Inequality from Annual Faculty Reports.” Journal of Higher Education 88 (5): 672700.CrossRefGoogle Scholar
O’Meara, KerryAnn, Lennartz, Courtney Jo, Kuvaeva, Alexandra, Jaeger, Audrey, and Misra, Joya. 2019. “Department Conditions and Practices Associated with Faculty Workload Satisfaction and Perceptions of Equity.” Journal of Higher Education 90 (5): 744–72.CrossRefGoogle Scholar
Wood, J. Luke, Hilton, Adriel, and Nevarez, Carlos. 2015. “Faculty of Color and White Faculty: An Analysis of Service in Colleges of Education in the Arizona Public University System.” Journal of the Professoriate 8 (1): 85109.Google Scholar
Figure 0

Table 1 Example of a Service Dashboard (Excerpts)

Figure 1

Table 2 Example of a Teaching Dashboard (Excerpts)

Figure 2

Table 3 Example of a Mentoring Dashboard (Excerpts)

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