EMCA4RJ: Who are we?

Members of the committee recently wrote a blog post for Research on Language and Social Interaction, explaining the need, aim, and focus of the group. The ROLSI published version can be found at: https://rolsi.net/2021/06/02/guest-blog-em-ca-for-racial-justice/ and features a clip of one of our data sessions

EMCA for Racial Justice


Eleonora Sciubba, Tilburg University                 

Natasha Shrikant, University of Colorado, Boulder 

Francesca Williamson, Butler University


The current social and political climate—involving anti-Black, anti-Asian racism, and anti-racist movements—has prompted widespread interest among academics about ways that racism, Whiteness, and structural inequality are pervasive in our own fields. EM/CA is one such field, and the authors of this post are members of a working group entitled, EMCA4RJ—or EMCA for Racial Justice—that was started in June 2020[2]. The purpose of this group is to foreground race and racism as central issues in the EMCA community.

EMCA approaches are well-suited for addressing racial justice aims through deconstructing how race and racism are constituted in everyday interaction. Some scholars, for example, have analyzed ways that broader phenomena such as racism, whiteness, or anti-racism occur through specific interactional moves like categorization (Shrikant, 2020; Whitehead, 2020) or extreme case re-formulations (Robles, 2015). More generally, however, race, racism, and racial justice have been understudied in the EMCA community. The purpose of this post, and of EMCA4RJ, is to encourage scholars to address racial justice through EMCA perspectives, which in turn will help grow the EMCA field in a way that is diverse, inclusive, and racially just. Below, we provide examples of ways that EMCA tools can be used to address racial justice aims in research, teaching, and community-building practices.

Research Practices

Within the EMCA community, unmotivated looking is a core shared research practice. Sacks (1984) argued that analysts should remain open to what could happen within conversations rather than limiting analyses to our imaginations or expectations of what should occur. However, as philosophers have argued, the practice of observation in scientific and social inquiry is tied to and shaped by researchers’ experiences, cultures, expectations, and academic training (e.g., Chalmers, 2013). In other words, who we (analysts) are and our experiences shape what we notice; what we can observe in social interaction.

We suggest a motivated looking approach that leverages tools within (categorization) and outside (race/racism) of EMCA. This approach involves taking racism, a social fact, as a starting point for inquiry. We can begin with searching for instances when social actors do race, examine how racial categories are implicated in social actions, and consider the interactional and social consequences of these categorization practices. To do this, we must first build racially and ethnically diverse EMCA research teams that focus on race and racism-in-interaction studies. Analysts who experience racism in their everyday lives may meet the unique adequacy requirement (Garfinkel, 2002) and can thereby improve our ability to notice and describe racialization and racism in interactions. Second, we should build collections focused on race and racism, as has been with gender (e.g., Kitzinger & Frith, 1999; Speer & Stokoe, 2011), to examine instances when racialization is achieved in interaction. As Rawls and Duck (2020) suggested: “[r]acism does not usually take an obvious form that we can see and prevent; rather it masquerades as the most ordinary of daily actions: as unnoticed and ever-present as the air we breathe.” (p. 1). Thus, racialization and racism are likely designedly ambiguous or elusive categorization practices that await our description. Building race-focused collections will help familiarize us with the varied ways racialization occurs and support analysts to better notice how it unfolds in interaction.

Third, we argue for increased methodological development of membership categorization analysis (MCA) to refine the tools we have for studying racism and other -isms. MCA has received less attention in the EMCA community, yet this methodological toolkit offers many resources for studies of racialization and racism-in-interaction. We could explore how specific category-bound activities or predicates are tied to racial categories and search for instances when race categories are positioned categories, for example. With a motivated looking approach, analysts could use MCA to examine how particular membership categorization devices (MCD) are produced in ways that may be marginalizing yet remain open to the ways that categorizations are produced and negotiated in interaction. Such studies could help us identify patterns in membership categorization that provide the interactional conditions for racism and its intersecting oppressions. Though some scholars have worked in this area within the EMCA community (e.g., Robles, 2015; Shrikant, 2020; Whitehead, 2020), more work is needed.

Finally, we should pursue new topics of inquiry through respecification projects as has been pursued for studies of social life in ethnomethodology (Garfinkel, 1991) and psychological constructs in discursive psychology (Potter, 2012), for example. This would require us to become familiar with the kinds of questions race scholars ask and explore how EMCA could pursue these inquiries. In what settings have scholars documented racial inequities? What interactional materials might be available for EMCA studies in these settings? These four practices – assembling racially diverse research teams, building race-focused collections, deepening MCA development, and cross-disciplinary engagement – would strengthen our contributions to understanding how race and racism are produced and for what purposes in social interaction.

Pedagogical practices

Conversation Analysis has a history of drawing teaching resources from audio and video data that are mostly anglophone, recorded either in the US or the UK, and feature White people, simply because they are already available and widely known within the discipline. Generations of CA practitioners have learned how to do CA on audio recorded data recorded in the 1960s and ‘70s: Sacks’ early work on calls to a suicide prevention line; Schegloff’s telephone calls among friends (Nancy, Hyla, Bea, etc.); Goodwin and Schegloff’s chicken dinner data. The problem with using “traditional EMCA data” for teaching is that it perpetuates a White, Anglocentric worldview that could make non-White students feel disconnected or excluded from the field. Furthermore, using this shared data set creates an ‘ingroup’ of people who know the data intimately, thus making it difficult for newcomers or those who did not receive “orthodox” CA training to enter the CA community. Moreover, it is difficult to draw connections between EMCA tools (e.g., adjacency pairs, categorization) and racism in these traditional data, as neither is readily visible as relevant, and the data are not taught or interrogated as examples of ‘Whiteness’.

We propose teaching EMCA through using data a) from linguistically and ethnically diverse people and b) where issues of race, racism, and intersecting inequalities appear as relevant. Teachers can draw from data collected by scholars who work with diverse participants such as Rawls and Duck (2020) (African American), Shrikant (2018) (Asian American), or Whitehead (2020) (makes Whiteness visible in South African contexts). Teachers can also draw data from current events where race and racism appear relevant (we provide an example below). We encourage teachers to be reflexive about representing a variety of ethnic and racial groups when teaching race and representing data from multiple different places in the world, as the ways race and racism operate vary based on socio-historical contexts. In addition to using diverse data, we also foreground the importance of the analytic lens for examining that data. We encourage teaching the ‘motivated looking’ approach introduced in the previous section. Last, we encourage CA teachers to draw on and value the expertise of their diverse students. In line with practices of inclusive pedagogy, teachers should treat the differences among learners as a strength for analyzing data. Asking students to reflect on ways they arrived at a particular interpretation of data aligns with ethnomethodological approaches to research (Garfinkel, 1967). To this end, EMCA4RJ is developing practical resources—a syllabus, a data set including audio/video clips and transcriptions, and suggested lesson plans—and will share these resources to help put inclusive pedagogy into practice for EMCA teaching.

Community Building Practices: The EMCA Data Session

Data sessions bring EMCA together as a community of practice, or a community defined and maintained through participation in shared practices with shared goals (Eckert & McConnell-Ginet, 1992). Data sessions occur frequently for research purposes—where people share audio or video recorded data and transcriptions with a group and analyze this data together—and for pedagogical purposes—to teach people how to analyze data from an EMCA perspective. Although data sessions are collaborative, they are not constituted by egalitarian relationships. In both types of sessions, participants do boundary work about the kinds of contributions to a data session that are considered reasonable, appropriate, or within the bounds of EMCA analysis (e.g., Antaki et al, 2008). Those who are senior faculty or EMCA insiders can set boundaries, whereas those who are junior scholars or generally newcomers to EMCA need to work within them. In some ways, clear boundaries and guidelines are useful, yet in other ways they are limiting.

Traditional conversation analysis data sessions do not address questions about race and racism. Many of us in EMCA4RJ have had the experience of attempting to make claims about racism in a data session only to be told that racism has ‘not been made relevant’ by the participants. Ignoring race, denying its relevance, or simply the inability to see race and racism in interaction are indicative of a White worldview (Bonilla-Silva, 2006). In EMCA4RJ, we challenge this worldview through conducting data sessions that leverage the tools from CA to deconstruct the ways that race and racism are made relevant in everyday interaction. During our data sessions, we help scholars support their noticings of race and racism through using EMCA tools instead of dismissing these noticings as outside of EMCA frameworks. We are also attentive to various skill levels of participants and encourage newcomer ideas. As part of EMCA4RJ, we are developing a document of guidelines for inclusive data sessions to share widely in hopes of encouraging other data session groups to operate in a more inclusive fashion.

Overall, we argue that the suggested research, teaching, and community-building practices will help transform the EMCA community to include more diverse scholars and more research on topics like race, racism, and Whiteness. It is in these ways that we can highlight ways that EMCA approaches can serve racial justice aims. 


Antaki, C., Biazzi, M., Nissen, A. & Wagner, J. (2008). Accounting for moral judgments in academic talk: The case of a conversation analysis data session. Text & Talk, 28(1), 1–30. doi: 10.1515/TEXT.2008.001

Bonilla-Silva, E. (2006). Racism without racists: Color-blind racism and the persistence of racial inequality in the United States. Rowman & Littlefield Publishers.

Chalmers, A. F. (2013). What is this thing called science (4th ed.). Hackett Publishing Company

Crenshaw, K., Gotanda, N., Peller, G., & Thomas, K. (Eds.) (1995). Critical race theory: The key writings that formed the movement. New York: The New Press.

Eckert, P., & McConnell-Ginet, S. (1992). Think practically and look locally: Language and gender as community-based practice. Annual review of anthropology, 21(1), 461-488.

Garfinkel, H. (1967). Studies in ethnomethodology. Englewood Cliffs, NJ: Prentice-Hall.

Garfinkel, H. (1991). Respecifcation: Evidence for locally produced, naturally accountable phenomena of order*, logic, reason, meaning, method, etc. in and as of the essential haecceity of immortal ordinary society, (I) – an announcement of studies. In G. Button (Ed.) Ethnomethodology and the human sciences (pp. 10-19). Cambridge University Press.

Garfinkel, H. (2002). Ethnomethodology’s program: Working out Durkheim’s aphorism. Rowman & Littlefield.

Kitzinger, C., & Frith, H. (1999). Just say no? The use of conversation analysis in developing a feminist perspective on sexual refusal. Discourse & Society, 10(3), 293-316. Retrieved from http://www.jstor.org/stable/42888261

Potter, J. (2012). Discourse analysis and discursive psychology. In Cooper, H. (Ed.), APA handbook of research methods in psychology: Vol. 2. Quantitative, qualitative, neuropsychological, and biological (pp. 111-130).Washington, DC: American Psychological Association Press

Rawls, A. W., & Duck, W. (2020). Tacit racism. The University of Chicago Press.

Robles, J. S. (2015). Extreme case (re) formulation as a practice for making hearably racist talk repairable. Journal of Language and Social Psychology, 34(4), 390-409.

Sacks, H. (1984). Notes on methodology. In J. M. Atkinson & J. Heritage (Eds.) Structures of social action: Studies in conversation analysis (pp. 21-27). Cambridge University Press.

Shrikant, N. (2018). “There’s no such thing as Asian”: A membership categorization analysis of cross-cultural adaptation in an Asian American business community. Journal of International and Intercultural Communication, 11(4), 286-303. doi: 10.1080/17513057.2018.1478986

Shrikant, N. (2020). Membership Categorization Analysis of Racism in an Online Discussion among Neighbors. Language in Society. doi: 10.1017/S0047404520000846

Speer, S. A., & Stokoe, E. (2011). Conversation and gender. Cambridge University Press.

Whitehead, K. A. (2020). The problem of context in the analysis of social action: The case of implicit whiteness in post-apartheid South Africa. Social Psychology Quarterly, 83(3), 294-313.

[1] This piece was a collaborative product, where all authors made equally significant contributions. We also would like to thank Jessica Robles for her thoughtful feedback on an earlier draft. Last, we drew many ideas from our participation in EMCA4RJ and from the following thread https://twitter.com/Nat_Shri/status/1298640326488395777?s=20.  

[2] http://emca4rj.conversationanalysis.org/; contact Natasha Shrikant (natasha.shrikant@colorado.edu) if interested in joining or learning more.

[3] See Video Clips, Transcript, and Excerpt of Data Session here: https://drive.google.com/drive/folders/145rI1ZI3lQF43DOxKTNJ-5cDK1f631OS?usp=sharing