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The Journal of Negro Education, 87 (3), 217-229

(Text)Mining Microaggressions Literature: Implications Impacting Black Computing Faculty

Fay Cobb Payton North Carolina State University Lynette (Kvasny) Yarger The Pennsylvania State University Anthony Thomas Pinter University of Colorado Boulder

Microaggressions are brief and commonplace daily verbal behavioral or environmental indignities (whether intentional or unintentional) that communicate hostility, insensitivity and negativity to an individual or group. Microaggressions communicate beliefs about who is expected to participate in and succeed in fields of study. Microaggressions can play a significant role in how Black faculty perceive and experience participation, engagement, retention and advancement. The authors adopt a broad definition of computing to include information systems, information sciences, and computer science. The Computer Research Association (CRA) Taulbee Survey, for instance, indicates that Black faculty represent 1.3 percent of computing sciences faculty at PhD-granting departments, and only 0.6 percent are full professors. Because Black faculty are woefully under- represented in the field, issues of career fit and progression, institutional fit, social and professional isolation, mentoring and career support are paramount. Understanding microaggressive experiences and coping strategies will advance knowledge and facilitate the development of theoretically-informed interventions for building the resilience of Black faculty and cultivating supportive institutional environments that encourage their retention and career advancement. The authors employ “big data” text-mining analytic methods to uncover and explore themes related to microaggressions experienced by Black faculty as discussed in scholarly and academic publications. The results uncovered five topics (jobs and race, gender and race, family, tenure, and dialogue) and three major themes (media coverage, post-aggression, and prevalence). The authors conclude with policy and research implications of these results. Keywords: computing, Black faculty, computer science, text mining, microaggressions INTRODUCTION Using data from the National Center for Education Statistics, Kena and colleagues (2015) report that Black men and women comprise 2.7% and 2.6%, respectively, of all associate professors in the United States. The numbers for full professor representation are even more dismal. Kena and associates (2015) report the following breakdown regarding faculty demographics:

In fall 2013, of all full-time faculty in degree-granting postsecondary institutions, 79 percent were White (43 percent were White males and 35 percent were White females), 6 percent were Black, 5 percent were Hispanic, and 10 percent were Asian/Pacific Islander. Making up less than 1 percent each were full-time faculty who were American Indian/Alaska Native and of two or more races. Among full-time professors, 84 percent were White (58 percent were White males and 26 percent were White females), 4 percent were Black, 3 percent were Hispanic, and 9 percent were Asian/Pacific Islander. Making up less than 1 percent each were professors who were American Indian/Alaska Native and of two or more races. (p. 227)

With the national focus on broadening participation in science, technology, engineering, &

medicine (STEM; National Science Foundation, & National Center for Science and Engineering Statistics, 2012, 2017), an investigation of Black faculty in computing can enable the field to uncover the effective coping strategies resulting in resilience of those who persist and advance in the field, and the microaggressions that can negatively impact promotion to the full professor ranks. The authors define computing broadly as does the Computer Research Association (CRA) 2017

218 © The Journal of Negro Education, 2018. Vol. 87. No 3

218 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

Taulbee Survey (Zweben & Bizot, 2018) and includes those fields of computer science (CS), computer engineering (CE) or information science (IS).

Per the 2017 Taulbee Survey, there are only 64 Black computer science tenure-track faculty across the United States at PhD-granting institutions. This represents 1.5 percent of total computer science faculty in the U.S. Of these 64, there are 17 full professors with merely 3 Black females and 14 Black males. There were 22 Black computer scientists at the associate professor ranks, and 25 Black assistant professors. None of these figures equated to more than a 5-percent representation across the totals. Prior work (Charleston et al., 2014) examined the 2012 Taulbee Survey (Zweben & Bizot, 2013) and reported similar representation, and concluded by describing these results as a disparity. This is particularly the case when these data are compared to 3.4%, 5.4%, and 6.4% of all Black/African American full, associate and assistant professors, respectively, across all U.S. degree-granting institutions including historical Black colleges and universities (HBCUs; Charleston et al., 2014).

The more recent 2017 Taulbee Survey (Zweben & Bizot, 2018) indicates that Black faculty represent 2.2 percent of computing faculty at PhD-granting departments, and only 0.7 percent of full professors are Black. By means of contrast, 59% of computing faculty are White, and 64.8 percent of full professors are White (see Table 1). Because Black faculty are woefully underrepresented in the field, issues of career fit and progression, institutional fit, social and professional isolation, mentoring and career support warrant exploration.

Retention of current faculty is critically important if the computing field is to meet the demands of its growing workforce, address innovation challenges associated with global competitiveness, and reverse the finding of the National Academy of Sciences (NAS):

Under-represented minorities comprise extremely low percentages in the natural sciences and engineering—biology at 6 percent, the physical sciences and engineering below 5 percent—and numbers so low in computer science as to make them practically nonexistent. (NAS, 2011, p.45)

Faculty of color can and sometimes do experience racial microaggressions and other barriers that can prevent or prolong career progression to the full professor ranks. As reported in Misra and Lundquist (2015), faculty of color, particularly Black, Latino and Native American, can experience the negative impacts on their careers from institutional racism, psychological departure and isolation. Furthermore, these microaggressions can have macro-impacts leading to implicit bias in the tenure and promotion processes, which can be characterized by unconscious assumptions that faculty of color are less qualified to move through the academic ranks from assistant to full professor, and some would argue into academic leadership roles.

Increasing faculty diversity has been a long-standing priority at universities and colleges across the United States. The widespread student protests over race relations in 2016 have made the issue of faculty diversity even more urgent as students call for increased institutional resources to hire more Black faculty (Thompson, 2015). Yet, one of the challenges that limit the participation of Blacks in higher education is racial microaggressions related to being a racial minority particularly on predominantly White institutions (PWIs). Research scholars (Bonner, Robinson & Tuitt, 2015; Cartwright, Washington, & McConnell, 2009; Constantine et al., 2008; Pittman, 2012; Sue et al., 2007) define microaggressions as subtle, stunning, often automatic verbal and non-verbal exchanges that are debasing to the recipient. These insults are often delivered through flippant verbal and nonverbal gestures and tones toward people of color, women, and other minorities, and can be intentional or unintentional (Solórzano, Ceja, & Yosso, 2000).

In Black Faculty in the Academy, Bonner and colleagues (2015) assessed the narratives of this group at PWIs. In this edited book, contributing scholars focused on lived experiences of Black faculty along three dimensions: (a) navigating daily encounters with racism; (b) multidisciplinary and intersectional meaning; and (c) effective mentoring as one approach to coping in stressful environments. Similar to the findings of Arnold and colleagues (2016), these dimensions offer insight and examples of how Black faculty experience microaggressions, such as obstacles in navigating the tenure process, constant questioning (respectability) of one’s scholarship and

218 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

Taulbee Survey (Zweben & Bizot, 2018) and includes those fields of computer science (CS), computer engineering (CE) or information science (IS).

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220 © The Journal of Negro Education, 2018. Vol. 87. No 3

220 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

capabilities among peers and students, avoiding the service saddle or service taxation, and challenges to maintaining mental, physical and spiritual health.

These experiences led to the following research questions: • What are the microaggression themes associated with Black faculty and those that can be

identified by text mining academic-based references? • What are the implications to the computing field?

LITERATURE REVIEW Racial Microaggressions Faculty of color often experience microaggressive behaviors both from colleagues and students (Misra & Lundquist, 2015). These experiences affect career trajectories, and the most significant regressions are seen at the associate professor ranks. One should expect that if faculty of color would move through the ranks, then one would anticipate a change in the composition of full professors (Misra & Lundquist, 2015). Others (Wong et al., 2014) conducted a literature review of 73 studies on racial microaggressions in research using PsychINFO. Of the 73 studies in a university context, only seven studies triangulated between qualitative and quantitative methods with different demographics and settings, including college students, community, university staff, Asian Americans, and adolescents. Only two studies (Cartwright et al., 2009; Constantine et al., 2008), however, examined the experiences of Black faculty between 2007 and 2012.

The first of the two studies on Black faculty noted in Wong and associates (2014) is Constantine and colleagues (2008). These researchers conducted 12 semi-structured interviews with tenure-track faculty in counseling psychology. Their sample included five (5) assistant, five (5) associate and two (2) full professors. They identified a number of microaggression themes, and subsequent studies also support these themes. For example, Sue and others (2007) documented these types of microaggressions in prior research and concluded that identifying mentors who have knowledge on the minority experience can provide a context for effective and relatable mentoring relationships if Black faculty are to be supported and promoted to the full professor ranks. Misra and Lundquist (2015) reported that extra service work like mentoring minority students and serving on diversity-related committees that is often undertaken by Black faculty is undervalued. The scholars concluded that service to the profession was shown to be more prestigious and more likely to be recognized. Mentoring, while a worthwhile cause, can be unrecognized and a significant investment of a faculty’s time, and it is the largest portion of service among women of color as shown in Figure 1.

Pittman (2012) found, when Black faculty experienced acts of subtle discrimination, they spent a great deal of cognitive energy trying to discern what caused the negative experience with their peer faculty, staff, and students. The process of contemplating these subtle social nuisances can have a salient impact on Black faculty experiences. Pittman (2012) also found that Black faculty can be overly concerned with their appearance. Failure to maintain an appearance, especially in clothing and hair that does not align with perceived norms can negatively impact both student rating of teaching effectiveness, and promotion and tenure decisions. Additionally, these concerns can negatively impact how Black faculty view campus climate and their ability to effectively conduct research, teaching, and service to move to full professor ranks.

While microaggressions have been studied in a number of contexts, the authors sought to discover from the literature the key themes as offered by text mining techniques, and discuss implications regarding the impacts to Black computing faculty.

Figure 1. Service and mentoring weekly hours of associate professors by race and gender. Reprinted with permission from Misra, J., & Lundquist, J. H. (2011). Gender, work time, and care responsibilities among

faculty. Sociological Forum. Available from http://works.bepress.com/jennifer_lundquist/4/ METHODOLOGY While microaggressions have been studied in a myriad of settings, including Black corporate professionals (Holder, Jackson, & Ponterotto, 2015 and faculty, the study seeks to use an emergent approach to discover from the scholarly literature the key themes using text mining techniques. Halper (2013) defined text analytics as:

Text analytics is the process of analyzing unstructured text, extracting relevant information, and transforming it into structured information that can be leveraged in various ways. Text analytics can use a combination of natural language processing, statistical, and machine learning techniques. Entities, themes, concepts, and sentiment are all examples of structured data that can be extracted from text analysis. (p. 3)

Researchers (Yaqoob et al., 2016) have detailed the current and promising trends of big data

techniques, including text mining, impacting research. As the volume of data continues to proliferate, vary in formats (digital, social, text, etc.) and is generated at high frequencies (velocity), diversity in data analyses methods has evolved. Much of these data offer a richer understanding into new areas of insights, and often overlooked or potential biases that can result in traditional qualitative data analyses, such as thematic patterns and independent coder agreement. The growing amount of unstructured, qualitative data poses challenges to manual analyses. However, these methods can be particularly useful in exploring social and data insights that exist in science, such as microaggressions among Black faculty.

Text mining enabled these authors to perform analyses on a corpus of microaggressions research studies. Each study served as a unit of analysis that was imported into the SAS Text Miner application (https://www.sas.com/en_us/software/text-miner.html). These studies were parsed and filtered to associate terms to parts of speech, build a dictionary for appropriate word lists, and evoke start and stop lists. Studies were placed into text clusters that are defined by a series of descriptive terms. This enabled authors to determine the studies that are associated by clusters by terms.

220 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

capabilities among peers and students, avoiding the service saddle or service taxation, and challenges to maintaining mental, physical and spiritual health.

These experiences led to the following research questions: • What are the microaggression themes associated with Black faculty and those that can be

identified by text mining academic-based references? • What are the implications to the computing field?

Researchers (Yaqoob et al., 2016) have detailed the current and promising trends of big data

222 © The Journal of Negro Education, 2018. Vol. 87. No 3

222 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

Because a predictive variable(s) was not forced, the results offer insight into emergent factors affecting career experiences among Black faculty.

Published literature was collected on microaggression from academic journals and newsletters that focused on racial diversity in higher education. This collection was comprised of two parts: (a) identifying potential sources and keywords; and (b) searching sources using the keywords and saving articles that met a set of criteria, and formed the corpus. To identify potential sources, both general search engines and a large mid-Atlantic university’s library database system were used, which had access to over 800 different databases. Through general searches and consultation with university librarians, the authors located one database, one academic journal, and three academic newsletters that focused on issues of diversity in higher education. All references were also searched that were included with relevant literature, creating sixth sample source. To account for different spellings of the word “microaggression,” each source was searched using the keywords “micro-aggression” and “microaggression.” Additionally, articles published prior to 2010 were excluded from the collection. This was to ensure that the articles in the sample were not out of date. Any article that was returned from either keyword and published post-2010 was saved and included in the corpus. Duplicate articles were deleted from the corpus to ensure an accurate sample size. RESULTS AND DISCUSSION In Table 2, we present the six sources, their publication type, and the number of articles that were yielded for the two keywords. Our total sample size was 135 useable articles (N = 135).

Table 2

Sources and Search Results in Published Literature using Keywords “Micro-aggression” and “Microaggression”

Source name Source type Relevant articles

Diverse: Issues in Higher Education

AN 19

The Chronicle of Higher Education

AN 30

Ethnic Newswatch DB 49

Inside Higher Education AN 30

Journal of Blacks in Higher Education

AJ 2

Reference Search RS 5

Total 135

To analyze the dataset, we employed SAS Enterprise Miner with Text Miner 12.1, a software suite for data and text mining. The process for analyzing a dataset in Text Miner 12.1 consists of 4 steps. First, we import the data set of interest and then, parse the text from the dataset, filter the text, and finally organize the text by topic and cluster.

Text Parsing The text-parsing node pulls each word from the supplied documents and assigns it a role (noun, verb, adjective, etc.) and an attribute (alpha, mixed, abbreviation, entity). The attributes refer to the importance of the word, so abbreviations and entities are unimportant, mixed may be important, and words characterized as being alpha are important. Text Filtering Next, the text-filter node determines which words to keep for analysis. The filter analysis drops words that are insignificant or common, like “the,” “be,” and “do.” After parsing and filtering the text, the two analytical nodes, text cluster and text topic, occur simultaneously on the remaining set of words. The text cluster node groups documents into disjointed sets based on the descriptive terms for those clusters, while the text topic node organizes documents according to both machine and human generated topics. Topics consist of several keywords that describe and characterize a central idea. The results from these two nodes are reported and discussed in the following sections. Text Topic The text topic node identified ten major topics in the data set. After removing five topics that were identified as not being true topics, we were left with five topics for analysis. The topics that were removed were either spam (“la,” “ca,” “la,” “rig”), or were about the sources themselves (“editorial,” “blog,” editorial-site”). These topics were removed from analysis because they did not provide insight into the type of discussions that were taking place about microaggression. Rather, they represented metadata about the sources themselves. While potentially interesting, examining this metadata is not the aim of this research.

The remaining salient topics are presented in Table 3, along with the number of terms identified and the number of documents containing those terms.

Table 3

Identified Topics from the Text Topic Node

Topic Name Terms Number of Terms

1 Jobs & Race Black, jobs 411

2 Family Child, parent, family, health 920

3 Dialogue

difficult dialogue, dialogue; difficult, racial dialogue

724

4 Tenure

Mentor, STEM, tenure, promotion, faculty

756

5 Gender & Race Black woman, Black man, racialized, gender

920

Total 3,731

222 ©The Journal of Negro Education, 2018, Vol. 87, No. 3

Because a predictive variable(s) was not forced, the results offer insight into emergent factors affecting career experiences among Black faculty.

Table 2

Sources and Search Results in Published Literature using Keywords “Micro-aggression” and “Microaggression”

Source name Source type Relevant articles

Diverse: Issues in Higher Education

AN 19

The Chronicle of Higher Education

AN 30

Ethnic Newswatch DB 49

Inside Higher Education AN 30

Journal of Blacks in Higher Education

AJ 2

Reference Search RS 5

Total 135

The remaining salient topics are presented in Table 3, along with the number of terms identified and the number of documents containing those terms.

Table 3

Identified Topics from the Text Topic Node

Topic Name Terms Number of Terms

1 Jobs & Race Black, jobs 411

2 Family Child, parent, family, health 920

3 Dialogue

difficult dialogue, dialogue; difficult, racial dialogue

724

4 Tenure

Mentor, STEM, tenure, promotion, faculty

756

5 Gender & Race Black woman, Black man, racialized, gender

920

Total 3,731

There are several interesting results from the identified topics, frequency of terms, and documents that merit discussion. First, there are two distinct race topic groups, “Jobs & Race” and “Gender & Race” that account for the highest document frequency and term frequency, respectively. Combined, these two topics account for 1,331 terms or 35.7% of the text topic sample. Moreover, these topics focus exclusively on Black faculty and students. The salience of articles and terms related to Black experiences of microaggression indicates that this broad topic is the most frequently discussed in the literature about diversity in higher education. The lack of other ethnicities in the main topics and the nodes reveals a research opportunity to explore the experiences of other underrepresented groups.

The second largest topic in document frequency was “Family.” These articles dealt with incidents of microaggression resulting from family and health issues. Given the demands of academic careers, it is not surprising to see that microaggression often occurs as a result of individuals sacrificing work for the sake of family or health. When viewed in tandem with the results of the “Tenure” and “Dialogue” topics, the connection to balance personal over professional life and microaggressions seems almost banal. Academic positions are uniquely stressful and require a high level of commitment to research, teaching, and service. When an individual goes against accepted norms and power structures, this could cause microaggressive behaviors that can impede career progression, movement through the academic ranks and in some cases, successful tenure outcomes.

The text topic analysis revealed several trends in literature discussing microaggression in higher education, and identified several areas for further inquiry. Next, we present the results of the text cluster analysis. Text Cluster Text cluster analysis identified a total of four clusters. Similar to the text topic results, one cluster was deemed to be either spam or metadata about the articles themselves. This cluster represented 21 percent of the sample and was removed from the analysis, leaving with three salient clusters. We present the remaining three clusters’ descriptions and document frequencies in Table 4.

Table 4

Emergent Microaggression Clusters

Cluster Name Description Document Frequency Percentage of Sample

1 Media Coverage

Articles that were about microaggressions and the impacts to faculty and students

52 36%

2 Post-Aggression

Articles that were about coping with microaggressions, including counseling and mental health

31 22%

3 Prevalence

Articles that focused on the prevalence and categorization of microaggression against various minority groups

28 21%

Cluster 1, “Media Coverage,” consisted of articles reporting on campus climate and are characterized by the SAS Text Miner application with the words shown in parentheses, such as University of Illinois Urbana-Champaign Survey (MostMinorityStudentsatIllinois) and the University of California system (UofCaliforniasProposed), among others (Colgate, Vassar). The study at the University of Illinois Urbana-Champaign found that over half of minority students experienced stereotyping (University of Illinois Survey). An article on the University of California education system dealt with student and faculty backlash against a proposed intolerance statement that was contradictory in nature, and thereby deemed as inappropriate for our corpus (UofCaliforniaProposal) given our topic focus. Other articles frequently focused on examining the incident rates of microaggressions, including students’ perceptions (StudentsSeeManySlights); what happened in the wake of an incident of microaggression (StunnedByAVideo; TheSupremeCourt); or what campus or government leaders could do to prevent incidents from becoming large protests (WhatCanCampusLeadersDo). The reporting was also frequently focused on the experiences of Blacks in higher education (WithFacultyDiversity; AsCollegeofCharleston). Much of the Black students’ social justice activism played out in social media, which provided a platform to discuss the negative impacts of their microaggressive experiences on their psychological and physical well-being and their academic performance.

In cluster 2, literature touched on the microaggression and post-exposure experiences of Blacks, Hispanics (Journal of Hispanic Higher Education, http://journals.sagepub.com/home/jhh), Asians, Native Americans (Jones & Galliher (2014)), and women (Hurtado & Figueroa, 2013). These map to several of the studies in the corpus and specifically Constantine, et al. (2008) and Sue (2004, 2007, and 2010) as listed in the References section of this manuscript. This literature, in contrast to the media coverage in cluster 1, demonstrates that microaggressions capture a broader group of underrepresented populations, with far-reaching physical and psychological health outcomes. The outcomes reported and discussed in this literature included adverse effects on personal relationships, difficulty reconciling multiple identities and sets of identities from different groups. The literature also reported coping strategies, such as using personal networks to talk about the experienced microaggression.

Finally, cluster 3 consisted primarily of academic literature concerned with defining microaggression and the prevalence of these behaviors. This literature included exploring the types of microaggressions that different groups experienced and the observations of faculty. In this cluster, we also observed scales and inventories to categorize the experiences that different groups have with microaggressions. Two scales, the Racial Microaggressions Scale (RMAS) and Inventory of Microaggressions Against Black Individuals (IMABI; Sterett, Zeigler-Hill, Wallace, & Hayes, 2011; Torres-Harding et al., 2012;), focused on Black experiences of microaggressions, while the third scale focused specifically on the experiences of Black LGBTQ individuals. The developed scales were validated as being reliable measures of microaggressions.

While several of the topics included in the media in cluster 1 were also identified in clusters 3 and 2, the articles in cluster 1 were media coverage of the phenomenon. Comparatively, the articles grouped into clusters 2 and 3 were academic literature that examined what individuals did in response to and prevalence of microaggressions, respectively. IMPLICATIONS OF THE MICROAGGRESSION CLUSTERS There are policy implications associated with our findings around the three microaggression clusters: Media Coverage, Post-Aggression and Prevalence. The media coverage cluster highlights social justice activism as a coping mechanism to counter microaggressions on university campuses. The focus on microaggressions in the media is understandable given the massive outcry for improved race relations and more diverse faculty at student protests across U.S. universities since 2015. College students, particularly those from underrepresented groups, mostly lead these organized protests on campus and on social media. While we studied microaggressions experienced by faculty, students communicated the importance of seeing people like themselves

Table 4

Emergent Microaggression Clusters

Cluster Name Description Document Frequency Percentage of Sample

1 Media Coverage

Articles that were about microaggressions and the impacts to faculty and students

52 36%

2 Post-Aggression

Articles that were about coping with microaggressions, including counseling and mental health

31 22%

3 Prevalence

Articles that focused on the prevalence and categorization of microaggression against various minority groups

28 21%

represented in the faculty. By improving diversity and inclusion, students hoped to gain more culturally receptive academic and social support among faculty. Social inclusion policies that foster faculty support along with equitable research, teaching, and service workloads are needed to reduce microaggressions and improve the institutional climate for Black faculty. These policies can, likewise, have positive impacts of the experiences of students (Nelson, 2007). This points to the need for continued institutional commitment paired with action to recruit and sustain Black faculty—particularly in computing and other disciplines where underrepresentation looms.

The post-aggression cluster represents the aftermath of the microaggressive encounters and how Black faculty can cope with the experiences. Institutional leadership signals how university mentoring programs, faculty support groups and climate policies are cultivated, implemented, and measured at all levels. Faculty (in particularly full professors), department chairs, deans, and executive leadership play vital roles in setting a tone of inclusion and demonstrated commitment as well as gaining faculty buy-in to endorse diversity programs (National Academy of Sciences, 2010). Beyond the mentoring, however, sponsorship of Black faculty can prove to an effective mechanism to foster inclusivity not just among faculty but institutional leadership as well.

While our prevalence cluster captured the theme of assessing microaggressions, institutional policies focused on campus climate can include these and other measures. These assessments can offer understanding on career barriers and recommendations to overcome impediments to diversifying faculty. This includes providing insights into faculty retention, campus and departmental climate, and career strategies to support and promote Black faculty. As reported in Misra and Lundquist (2015), faculty of color can experience adverse impacts on their careers from institutional racism, psychological departure, and isolation. Furthermore, these microaggressions can have macro-impacts leading to implicit bias in the tenure and promotion processes, which can be characterized by unconscious assumptions that faculty of color are less qualified to move through the academic ranks.

IMPLICATIONS TO COMPUTING

Difficult dialogue was identified in the literature as a necessary step in healing racial fissures and improving the campus climate. Sue (2004) has stated that the greatest challenge to combatting racial microaggressions is “making the ‘invisible’ visible.” That can only be accomplished when people are willing to openly and honestly engage in a dialogue about race and racism. The slow promotion rate of Black computing faculty has significant consequences, and speaks to the need to learn from those who have opted for alternative career paths or have abandoned the traditional academic faculty roles (National Academy of Sciences, 2011).

The low numbers of tenured Black faculty and challenging institutional climates can negatively influence those currently in the computer discipline as well as Black and other underrepresented students who could engage along the academic pipeline and broadening participation in the field. This raises the question of how computing departments and leadership (department chairs, deans) can be intentional in addressing these issues to embrace innovation and inclusive strategies impacting the computing pipeline (particularly at the undergraduate level) and faculty experiences.

Accordingly, Young, Anderson, and Stewart (2015) used “hierarchical microaggression” to depict microaggressions that occur from the systemic (de)valuation of an individual’s institutional role (e.g., junior faculty, administrator or staff). While,

hierarchical microaggressions exist in all workplaces, but are of a unique type in a university because of the rhetoric related to equality and upward mobility . . . they impact people because people take on an identity associated with their status at the university. (Young, Anderson, & Stewart, 2015, p. 68)

Nelson (2007) concluded that faculty demographics impact the ethnic composition of the student population. A lack of racial and ethnic representation within the faculty can adversely affect minority student success, influence perceptions of an inhospitable campus climate, and hinder the creating of mentoring relationships as students often seek out faculty of similar backgrounds and

characteristics. We espouse that preparing the field’s current and future faculty for the intricacies of academic life and career trajectories should not be absent of preparing the discipline for the broadening participation needed to address underrepresentation. In a study of business, computer science and history disciplines, “faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality” (Clauset, Arbesman, & Larremore, 2005, p. 1).

CONCLUSION The academy is challenged with addressing the microaggressions found in the literature. While our resulting five topics (i.e., jobs and race, gender and race, family, tenure, and dialogue) speak to what we uncovered from academic reports and studies, the three major themes (media coverage, post-aggression, and prevalence) suggest that growing media attention on racial incidents is furthering useful but difficult conversations about race in the academy. In addition, a hostile and invalidating campus climate can reinforce the microaggressions experienced by Black faculty. However, institutional and departmental leadership that make diversity and inclusion core to their educational mission can improve the campus climate, mental and physical health, and productivity of Black scholars.

Microaggressions research can offer knowledge on career barriers and recommendations to overcome these impediments to diversifying computing departments. This includes providing insights into faculty retention, campus and departmental climate, and midcareer strategies to support and promote Black faculty of computing. Hence, computing departments are challenged to adapt more systemic approaches to broadening participation—thereby enabling intentional assessments of departmental, college and the roles of full professors rather than seeking to “fix the faculty” as a sole solution. While prior research (Toldson, 2018) has offered guidelines relative to HBCU students that pursue doctorates in STEM fields, these findings can, in part, cultivate supportive academic environments and, likewise, be applied to the experiences of Black faculty. The practice community can benefit from the findings as Black faculty can provide a catalyst for mentoring underrepresented undergraduate and graduate students to extend those likely to pursue graduate degrees in computing and select their optimal career pathways. For funding agencies, this research can inform and challenge guidelines associated with broadening participation in computing and (re)consider how field abandonment and career stagnation impacts progress in computing. Each of these points is important for public and higher education policymakers in an effort to support and implement policies to both inform and encourage public engagement in computing innovation, workforce development and foundation, corporate and higher education initiatives with programs such as NSF INCLUDES, https://www.nsf.gov/news/special_reports/nsfincludes/index.jsp and Google for Education, https://edu.google.com/computer-science/?modal_active=none.

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IMPLICATIONS TO COMPUTING

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AUTHORS FAY COBB PAYTON is Professor, Information Technology and University Faculty Scholar at North Carolina State University, Poole College of Management, in Raleigh. LYNETTE (KVASNY) YARGER is Associate Professor, IST at The Pennsylvania State University, College of Information Sciences & Technology in University Park. ANTHONY THOMAS PINTER is a doctoral student in Information Science, Department of Information Science, College of Media, Communication, and Information at the University of Colorado Boulder. All comments and queries regarding this article should be addressed to [email protected]