Difficult Discourse: Searching for a Critical Generosity

This seminar considers critical practices in both the sciences and humanities and their ability to produce forms of meaning. Where is meaning located? What kinds of interpretive methods produce truly meaningful ideas? Is it more valuable to reveal “new” perspectives or verifiably true ones? Is it possible to have two conclusions from the same evidence? Do the “academic” or “scientific” strategies of meaning creation apply to our everyday means of thinking? Do we have a political responsibility to champion particular kinds of critical practices because of the conclusions they render?

To approach these questions, we will focus on two essays: Stephen Best’s and Sharon Marcus’s 2009 essay “Surface Reading: An Introduction,” and Bruno Latour’s 2003 lecture “Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern.” Of particular interest to our discussion will be the status of “gaps” or “anomalies” in either literary representation or scientific theory and their effects on knowledge formation. We are also curious if anti-symptomatic methodologies such as “surface reading” effectively move us away from an unhelpful preoccupation with “secrets” and political motivations that obscure the search for more objective forms of meaning.

This seminar will discuss the theoretical motivations and implications of data-centric approaches to the study of history, literature, and the humanities in general. We hope to take a bifurcated approach to this question, addressing both the potential problems/limits of Big Data, as well as the ways in which its approach offers productive avenues for inquiry. Among the questions we hope to consider are the following:

• How do Big Data methodologies in the humanities explain change?

• How are human actions understood?

• What value, if any, (is/should be) given to human agency? phenomenological experience?

• How might Big Data accommodate the human, how might a shift in scale or focus affect its methodology?

• What makes Big Data possible? What are its epistemological assumptions/implications, and how separable are these from its “tools”?

• What is the status and function of meaning and interpretation in Big Data methodologies?

• If we didn’t have Big Data to rely on, how might we answer questions of long duration?

What defines the boundary of a discipline, and what happens when academics reach the edge of what they can learn using the methods admitted by their field? In this seminar, we will approach these questions from an example in theoretical physics.

Often touted as the pinnacle of objective science, it is a field some prominent scientists think is in crisis. Decades of research in theoretical, high-energy physics developing ideas related to string theory and the multiverse have so far failed to result in a well-defined physical theory or any hope of empirical validation for the postulates underlying them. By most conceptions of how science progresses, these theories lack the characteristics of successful research programs. While interesting on their own merits, can these programs promise scientific knowledge? In the possible absence of better alternative theories, how does the field best move forward?

Some prominent proponents of these theories are advocating a new paradigm of ‘post-empirical science,’ extending traditional understandings of the scientific process to allow for theory confirmation without empirical evidence. They claim that mathematical consistency and elegance, the lack of alternative theories, and previously demonstrated theoretical successes can be sufficient to confirm a new scientific theory in the absence of any conceivable experimental test. But if skepticism is an integral part of the scientific ethos, then does this not amount to just giving these theories a pass, and risk distracting from rigorous investigation of currently un-imagined alternatives?

Skeptics have raised the concern that in an era of eroding public trust of science, removing this distinguishing factor of scientific progress risks undermining the basis of trust that science offers. In addition, often over-hyped claims of pending success in these programs is seen to have drawn a disproportionate amount of research funding, attracted many of the brightest minds in the field, and convinced the public these are firmly established scientific dogma. Is it healthy for the field to devote so many resources and so much talent to theories which have largely failed to make contributions back to the broader Physics community? How should the hypothetical nature of these ideas be communicated honestly to the public?