As the sociologist Laura Nelson observes, machine learning and research into culture are scholarly modes of inquiry which have developed in largely orthogonal ways, but which are epistemologically aligned. As such, integrating these approaches in ways attentive to this alignment has the power to more fully realise the potential of computational literary analysis (2). Moreover, this kind of cross-disciplinary work does not freeze or limit epistemological frameworks but opens them up in productive ways, allowing researchers to, as Clayton Childress puts it, move our methods beyond triangulation and confirmatory to mechanisms for “propulsive facilitation” ranging “across methods and data sources in which epistemic standpoints change as we go” (980). These calls from the social sciences resonate with those from within computational literary studies for scholars to do what Richard Jean So and Edwin Roland describe as “the hard work of developing a critical version of distant reading” (72) capable of rising to the challenge of analysing the dynamic and contingent terms through which human identities, subjectivities and bodies are represented and categorised, in ways which are sensitive both to historical specificity and to the workings of literary form and genre.
Situating itself within this line of digital humanities work, this paper takes up the word abortion, a term whose meanings and associations vary considerably across domains and across historical and cultural contexts, and considers the utility of machine learning for understanding the work it does, and the associations it carries, in different discursive environments. Emerging out of a dissatisfaction with the connotations of failure and malfunction that accrete around the term abortion, it brings together perspectives from queer theories of negativity (eg. Halberstam) which provide a framework for interrogating the conceptual underpinnings of those accretions, and work from literary history exploring ‘the abortion metaphor’ as it is articulated and fleshed out in novels and other writings of the early twentieth century, together with word embeddings, a technique from natural language processing in which words in a corpus are mapped to low-dimensional vectors and the relationships between these vectors are then used to reveal latent semantic relationships between words (Antoniak and Mimno 107). We argue that applying machine learning methods to the term abortion presents two key methodological innovations. First, they offer a way to grasp the diversity of contexts in which the term has appeared, from embryology to literary aesthetics, and suggest how these illuminate different aspects of failure or non-completion. Second, using vector algebra to subtract negative associations from the term allows us to explore the discursive terrain abortion occupies when those associations are removed. If word embeddings are an effective way of capturing the manifold contexts in which a word appears, abortion presents itself as a particularly intriguing example for literary scholars and those in the medical humanities to interrogate, inflected as it is with highly positive connotations in some contexts and highly negative ones in others.
Works cited
Antoniak, Maria, and David Mimno. ‘Evaluating the Stability of Embedding-Based Word Similarities’. Transactions of the Association for Computational Linguistics, vol. 6, no. 0, Feb. 2018, pp. 107–19.
Childress, Clayton. ‘Bringing Computation into Cultural Theory: Four Good Reasons (and One Bad One)’. New Literary History, vol. 54, no. 1, 2022, pp. 975–83.
Halberstam, J. The Queer Art of Failure. Duke University Press, 2011.
Nelson, Laura K. ‘Leveraging the Alignment Between Machine Learning and Intersectionality: Using Word Embeddings to Measure Intersectional Experiences of the Nineteenth Century U.S. South’. Poetics, vol. 88, Oct. 2021, p. 101539.
So, Richard Jean, and Edwin Roland. ‘Race and Distant Reading’. PMLA, vol. 135, no. 1, Jan. 2020, pp. 59–73.