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Text Analysis, Structural Power, and Structural Inequalities

Lauren Klien’s “Distant Reading after Moretti” offers a number of points of departure relating to the humanities as a whole, particular disciplines, and the general nature of computation. As an expanded version of our reading “Gender and Cultural Analytics: Finding or Making Stereotypes?”, Klien references Laura Mandell’s revelatory presentation in 2016 at the University of Michigan Library.  In the presentation, Mandell expands her analysis of gender and stereotypes to include discussions about Google and various OCR efforts.  Her exposures of major biases that completely distort interpretations and studies related to gender, and which occur at all levels of textual research and analysis from the problems of optical character recognition to the misuse of statistical techniques, argue for the importance of carving out an entire subfield or ongoing set of research initiatives dedicated to the critique of computation, along the lines of critical computation studies.

Not all schools of thought within the humanities may advocate for “connect[ing] the project of distant reading to the project of structural critique” or for actively supporting demands for social justice prompted by institutionally sanctioned practices of abuse and dehumanization within academic organizations. But given an academic landscape characterized by humanistic pluralism, scholars such as Klien and Mandell point to the possibilities of building forceful and lasting foundations of rigorous and critical scholarship for academic communities committed to socially engaged and progressive values, foundations which can serve as the leading-edge in the project of exposing and interrogating power.

As one example of how the debates about representativeness, statistical significance, and bias in textual corpora can help methodological critiques in other disciplines, the ways in which historians generalize with broad brush strokes using terms such as “everyone” or “no one” in relation to an entire polity or culture appear less defensible given the construction of albeit problematic digital archives totaling potentially billions of texts and artifacts. Important and crucial skepticism about the archiving and analysis of texts leads to important skepticism about generalizations and abstractions, whether theoretical or empirical, quantitative or qualitative.

In terms of the nature of computation in general, questions that come up include: Is there hidden performativity behind the act of enumeration that gives quantitative analysis the chimera of ideological prestige? To what extent if any do the socially constructed dichotomies between computational work and the traditional work in the humanities (or between the digital and the analog, etc.) reflect the functions class, gender, race, and education within the context of private capital accumulation? Ted Underwood and Richard Jean So underscore the value of experimental, iterative, and error-correcting models and methodologies in computational research. To what extent would a commitment to these approaches address the issues Mandell raises about the problems of text as data regardless of computational techniques?

One thought on “Text Analysis, Structural Power, and Structural Inequalities

  1. Asma (ahs-ma) N.

    Yes to multiple points here! Especially this line on skepticism, to which I feel is a response to your closing question: “Important and crucial skepticism about the archiving and analysis of texts leads to important skepticism about generalizations and abstractions, whether theoretical or empirical, quantitative or qualitative.”

    I think commitment, on some level to what the authors name, is ethical and thus crucial to producing research as DH-ists. How that translates to the overall field, institution and whether or not both accept newer suggestions for text analysis is one I would manage with the same Skepticism asked of us to consider this week. My only challenge (at the time of this) is that Underwood, Witmore, etc. didn’t say anything particularly new to audiences on the margins who already consult skepticism in their fields, analyses and so on. Gallon and Daut speak to how similar modes of re/analyzing data (computationally and methodologically) have been deleterious in sizable ways (even if from a different angle) named by the authors this week.

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