Live Discourse Analysis

While rhizomatic-antenarratives are only one type of antenarrative, Boje’s (2001a) antenarrative methodological monograph, Narrative Methods for Organizational and Communication Research, appears Deleuzian in its approach. Boje does not assert prescriptive methodological procedures but rather proceeds through eight chapters in a homology of Deleuze and Guattari’s (1987) A Thousand Plateaus. Boje’s methodological treatise therefore does not impose a “correct” methodology[1] but rather aims to productively destabilize common analytical approaches to narrative research within organization studies. We can distinguish such an ante-methodological approach from an anti-methodological approach that eschews methodological coherence altogether[2]. This also distinguishes antenarrative from antinarrative, as the former aims to bring narrative and story into a flat ontology through an antenarrative bridge while antinarrative eschews coherent narrative modes of thinking and being altogether (Boje, 2001a, p. 2). As Boje (2001a, p. 41) mentions, referencing Deleuze and Guattari, his goal is not to reject foundationalism altogether, but to draw greater attention to the “materiality of desire and rhizomatic linkages of thought to the world of flows.” This also affords the materiality of narrative (p. 11). Boje’s monograph, therefore, was written as resistance to the predominance of thematic, structural analyses within organization studies (Czarniawska, 1997; see also Section 3.1.4.)

Boje’s (2001a) eight antenarrative “plateaus” include:

1. Deconstruction. Boje (ibid.) describes deconstruction as antenarrative in action, a practice of challenging the ontological assumptions stratified within narrative—a way of destabilizing the semiotic center of meanings and stories (Derrida, 1979). In this sense, the analyst joins in the antenarrative by becoming part of the ongoing textual deconstruction of interpenetrating processes and weaves of restructuring, unraveling, and constructing stories. In this way, antenarrative researchers are not independent of the organizations that they research but are rather active participants. (See Miller et al., 2012 for an example in STS.) Boje draws substantially from Derrida (1979) when outlining this approach.
2. Grand Narrative. Grand narrative draws from Lyotard (1984), who discusses grand narratives—or, in Deleuzian terms, major narratives or literatures—as macro-level overarching narratives. An antenarrative approach to grand narratives questions such macro narratives through contradiction and exception (Boje, 2001a). The ambition is to shatter grand narrative into many small stories and to problematize any linear, mono-voiced grand narrative of the past by replacing it with an open, polysemous (many meanings), and multi-vocal web of little stories. Put differently, “to analyse, resituate and restory grand narratives, then, is to let a thousand stories bloom rather than dismiss certain stories as unworthy” (Boje, 2001a, p. 44). Boje, therefore, seeks not to reject all grand narratives, but to complicate them through overlooked or silenced stories.
3. Microstoria. Such little stories constitute microstoria — a preference for the oftentimes hidden or suppressed stories that are subsumed by grand narratives. Microstoria research, in other words, prefers local antenarrative knowledge, the little people’s histories, and seems to ignore the macro narrative “great man” accounts fashionable in organization studies. Such accounts also parallel microstorians’ tendencies to boldly question grand narratives of capitalism and Marxism in favor of “local ways of knowing [often] delegitimated by macrohistory, science and progress, and development myths.”
4. Story Network. Story Network takes antenarrative practices into a networked account of an organization, bringing stories into focus and discussing not just how stories encounter each other, but with whom and at what time. This mirrors some applied linguists’ analysis of textual trajectories, or the ways stories (or more broadly, texts) are both (1) recontextualized and (2) transformed and thereby entextualized by actors of distinct power relations and scales (see Ehrlich, 2012; Kell, 2013, 2017; Maybin, 2017). The organization is therefore seen as a storytelling system in which stories are the medium of exchange (Boje, 2001a).
5. Intertextuality. Intertextuality, on the other hand, posits its own antenarrative network — a dialogic conversation among writers and readers of texts. Boje, in this case, draws from critical literary scholars like Barthes (Allen, 2004), Kristeva (1986), or Bakhtin (1981) to discuss how the living stories of participants within an organization are imbued not only with the stories of those within the organization, but also stories that exist outside of the organization — therefore positing a polyphony of voices and living, stereographic, textual system (Boje, 2001a).
6. Causality. Causality uses antenarrative to upset commonly held beliefs of causal agents. Boje (2001a) theorizes the causal field as messy and often unfathomable, and acts of narration camouflage the antenarrative fabric. “In the postmodern world of storytelling organizations linear causality is a convenient fiction, an over-simplified narrative of complex antenarrative dynamics” (p. 14). In other words, antenarrative researchers seek to find causal links held to be natural and seek to study the non-linear antenarrative pathways of story reconstruction and retrospective sense-making as an alternative to linear causal mapping.
7. Plot. Plot adapts Ricoeur’s (1984) theory of emplotment – the organizational conditions of narrative that produce gaps or cracks for antenarrative to unsettle. In other words, when one looks at the grand narrative of an organization, one must not only think about the meaning of the narrative, but also how the constituent parts of that narrative are strategically deployed. “Plot forms a structural basis for analysis…but it is a narrow and reductionist analysis. To focus only on plot takes us down a linear pathway in our analysis” (Boje, 2001, p. 121). Antenarrative seeks to upset this internal, inscribed narrative logic.
8. Theme. Finally, this approach opposes taxonomic classification. Boje considers taxonomic cells as “little narrative cells to trap stories” (Boje, 2001a, p. 11). An antenarrative approach to theme, therefore examines “what gets left out of the themes and taxonomy cages and what goes on between cells. What is beyond the map?” (p. 125). This thereby constitutes Boje’s response to practices of thematic coding or grounded theory, which seek to extract slices of human experience and place them into theorized and socially constructed boxes (Packer, 2017).

I consider Boje’s methodological meditations antemethodologies because (1) they draw from diverse established methodological traditions in discourse analysis and narrative inquiry yet (2) they aim to resist such established traditions by using antenarrative as a destabilizing agent.

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[1] However, Boje and Rosile (2020) also discuss data gathering procedures in the form of conversational storytelling interviews. Therefore, the present discussion focuses on Boje’s apparent resistance to methodological procedures for analysis and presentation of data, rather than data collection.

[2] See Feyerabend (1974) for a more anti-method discussion. Boje, a generous citer of theoreticians, has very scant references to Feyerabend despite their surface-level similarities.

Ontological Reflection

While I adapted these eight “methods” from Boje’s 2001 monograph, Narrative Methods for Organizational and Communication Research, what I find most challenging about antenarrative theory — and also most exciting — is its inability to be placed into a series of checklists, checkboxes, or rubrics. I don’t see this as an accident. I think that it’s intentional. It’s part of what antenarrative is.

Antenarratives are future-looking bets, sense-making towards the future, while Weick talks about sense-making as a backwards-looking, retrospective account of ambiguity or crisis. Antenarrative turns towards the future to make sense of how each and every one of our living stories is always in motion. To be in motion means for there to be a projected future location as determined by the direction and magnitude of the vector of our storytelling. And when I think about antenarrative in this way, it only makes sense for antenarrative methods to refuse a kind of checklist or to-do list format — because antenarrative methods don’t aim to prescribe action at all. Antenarrative methods encourage us to take methodological and epistemological assumptions that many of us hold dear and challenge them.

One of my favorite antenarrative methods has been deconstruction — challenging, particularly, the definitions of story and narrative within applied linguistics research. Story and narrative are often used interchangeably, but when we start to look closer at the ways that narrative and story collocate and run with different word fellows, what we see is narrative being associated with abstract, textual, and aggregated stories that are petrified and simplified into a coherent and rigid structure, while story, on the other hand, is more often associated with pedagogy — specifically the pedagogy of teaching children. In other words, storytelling as a method oftentimes speaks to early childhood education in the ways that teachers use storybooks in order to encourage language development in their students. However, what does it mean for researchers to not assume the role of narrative analyst — the more common, professional, one might say adult-sounding term — but to perhaps regress into the role of a storyteller? I would argue that this would not be a regression but rather a progression: a forward-looking bet into a research world currently consumed with the flames of the Uncanny Valley.

What I mean by the Uncanny Valley is the role of generative AI and the upsetting of the status quo that always assumed that writing was a human-generated endeavor. Now that we have computers that can produce text, or edit and simplify our writing — oftentimes into a homogenized, sampled whole — we now have to wonder what it is that humans can do with language that AI might not be able to. There are often simple propositions that people make, such as the fact that AI doesn’t actually use language at all; it is rather a prediction machine. But I find this to be a more semantic argument. I think the real question is: does AI make actual bets on the future, and does AI promote change in language? I would argue that AI can suggest changes in language, but it involves human judgment in order to make those changes stick.

There’s a lot of plotting that people are doing — some of it catastrophic plotting — in order to make sense of where AI is going in relation to academic writing. What antenarrative methods remind me is that the destination is not predestined, that the death of academic writing and of academia as a whole is not written in stone. It represents a simplistic, linear plot in which we see AI adopting a linear path of consumption and growth, where AI assumes more and more roles and humans assume less and less. But I think this oversimplifies the plot. The truth is, I have no idea what AI will look like even ten years from now, or what the human-AI cyborg interface will enact ten years from now. And anybody who seems to preach a certainty of either utopia or doom is hard to trust — because we always have to ask what is being erased, intentionally or not, when we’re thinking about the AI plot.

Those that are overly optimistic might be missing and erasing problems of environmental degradation — and John Maynard Keynes’ famously incorrect prediction that being more productive will actually save us time. Being more productive does not save us time. Being more productive simply allows us to be more productive and to produce more. The expectations will rise, and we will be busier than ever. Yet also, those who predict Skynet often miss the ways that technological innovations produce non-linear, knock-on effects in the ways that people work and live. The scale of this change makes it feel the most alarming. And I think this is why people often use the word unprecedented. But I think we have to question how unprecedented this really is. The problems in academic discourse were not precipitated by AI; they have simply accelerated.

Ever since the early 19th century, when Thomas Malthus predicted a global catastrophe of overpopulation, we have yet to realize such an apocalypse. The apocalypse that we will witness may or may not happen, but it certainly will not take a shape that we can predict — until it’s too late and we realize that we have seen the writing on the walls years ago. But that retrospection will ultimately be a product of hundreds of thousands, and millions, of microscopic prospections that all of us make every single day: the bets on the future that all continue to aggregate and congregate and shift the trajectory that we as academics will continue to take. Small degrees of refractive change can compound into massive shifts in trajectory.

The future is coming — but the future also starts now.