Alex Dayer

Advances in machine learning hold promise for corpus analysis: they have the potential to allow for more efficient and less biased analyses of text. This would be a boon for qualitative research, such as the survey research conducted by Academic Philosophy Data and Analysis. In this paper we examine the utility of automated machine learning for select survey questions, with a focus on LDA and VADER. We thus compare human and machine coding on the question of whether underrepresented philosophers are more likely to respond negatively to questions concerning diversity and inclusivity in academic philosophy. Our study has mixed results, revealing instances where automated machine learning can be used to fruitfully support and interrogate more traditional hand-coding techniques, but also instances where we were unable to glean much insight. (Contact authors for draft.)

Doctoral graduates in philosophy are an excellent source of information about the discipline: they are at the cutting edge of research trends, have an inside view of researchfocused departments, and their employment prospects provide early insights on the future health of the discipline. We report on the results of a survey sent to recent PhD graduates and current students, as well as data gathering efforts by Academic Placement Data and Analysis that have taken place over the past ten years. In this report we especially focus on demographic representation, program ratings, academic job placement, and non-academic careers.

Peak human performance—whether of Olympic athletes, Nobel prize winners, or you cooking the best dish you’ve ever made—depends on skill. Skill is at the heart of what it means to excel. Yet, the fixity of skilled behavior can sometimes make it seem a lower-level activity, more akin to the movements of an invertebrate or a machine. Peak performance in elite athletes is often described, for example, as “automatic” by those athletes: “The most frequent response from participants when describing the execution of a peak performance was the automatic execution of performance”. While the automaticity of skilled behavior is widely acknowledged, some worry that too much automaticity in skill would challenge its ability to exhibit human excellence. And so two camps have developed: those who focus on the automaticity of skilled behavior, the “habitualists,” and those who focus on the higher-level cognition behind peak performance, the “intellectualists.” We take a different tack. We argue that skilled behavior weaves together automaticity and higher-level cognition, which we call “pluralism.” That is, we argue that automaticity and higher-level cognition are both normal features of skilled behavior that benefit skilled behavior. This view is hinted at in other quotes about automaticity in skill—while expert gamers describe themselves as “playing with” automaticity, expert musicians are said to balance automaticity with creativity through performance cues: “Performance cues allow the musician to attend to some aspects of the performance while allowing others to be executed automatically”. We describe in this paper three ways that higher-level cognition and automaticity are woven together. The first two, level pluralism and synchronic pluralism, are described in other papers, albeit under different cover. We take our contribution to be both distinguishing the three forms and contributing the third, diachronic pluralism. In fact, we find that diachronic pluralism presents the strongest case against habitualism and intellectualism, especially when considered through the example of strategic automaticity. In each case of pluralism, we use research on the presence or absence of attention to explore the presence or absence of higher-level cognition in skilled behavior.