Seizing the helm

Hijacking The Ivy League Podium pt. 0

It’s been a while since I have posted here simply due to an unfocused lack of motivation. I’m not sure what my “audience” is, or what I want it to be… if I should rant about academia or pop culture, skateboarding or economics, or if blogging is even still fun to read or rather annoying spam mail…

For now these dilemmas are solved by remembering that I started this newsletter because my students at Columbia and NYU asked me to. So now, Hijacking the Ivy League Podium continues. Or rather, takes a step backwards. Having spent a jobless year sorting out my career, searching for positions, and watching the job market unfold in a mix of familiar and unfamiliar directions, my mind has been forced to return to a place surely of utmost interest to all of my students:

How does one even get an academic job? How did someone like me land at Columbia?

Some days I feel I actually only pretend to have answers to these questions. On more confident days like today, as I continue to find some relative success in a notoriously treacherous job market while also watching the Ivies continue to hire more and more teaching faculty like me, I do believe that there is a clear critical history of neoliberal universities that should be discussed in considering academic job market technique. I do indeed personally hold a distinct, albeit bureaucratic and boring, method to the madness behind seizing the helm of the podium, apart from the generic nepotism of all professions and the desperately needed critical scholarship to steer the ship in new directions.

Getting a teaching faculty position

I am a teaching faculty: I get hired to teach. I tell my department that I focus on teaching, and that I will likely not be publishing academic articles at any sort of pace that is competitive with the publication standards of a tenure-track professor position. I will probably never receive official tenure. Most professors of all kinds, from my observation, have not purposely followed my path. Rather, the common approach to academia is to monotonically aim for a tenured position at Harvard, and hopefully feel satisfied with whatever spot up the ladder we inevitably fall off. For me, the tremendous workload and ideological narrowness needed to sustain such a career-path of endlessly editing unreadable formalism is simply undesirable. However, as long as it is a possible career I will aim to teach at the university level, to help facilitate the burgeoning professional conversations of eager young adult American students.

Basic labor economics actually helps understand my small but growing job market. Within the neoliberal bullshit university, where management is endlessly lazy and enrollments endlessly rising, teaching positions are going to be available to credentialed faculty with good evaluations in courses with high enrollment. Our job on the market is to prove to individual academic departments that we can teach the high-enrollment courses requested (high labor demand) by the university administration’s requirements of our individual discipline. Such broad courses are not easy to fill with narrow research-focused faculty (low labor supply), so large general and introductory courses fill the bulk of short-term “teaching needs” (excess labor demand) in most academic departments. The hiring committees for teaching jobs are thus looking for someone who can reliably satisfy university demands without causing them too much trouble: someone with a respectable PhD who can ideally make a genuine case that they are a passionate teacher and happy to let go of the Harvard-peaked American research ladder (although, I would argue that my own case is proof that the teaching ladder is probably easier to climb than the standard research one).

Getting into a PhD program

Too much has been said on the obvious advice for eager PhD applicants: more doors will open if you score as perfectly as possible on as many exams as possible and work on as much “research” with the most published researchers.

The trickier questions to me, as a curious scholar who also wants to maintain a job that can pay rent for a small family, lie in the specifics of our “methods” and “disciplines”. The academic record continues to prove that different departments are more ripe for careers and scholarly opportunity than others.

The real challenge of “liberal education” is getting into a “respectable PhD program”, and pretending that we can predict the credibility of the academy over time. Plenty has been written about the increasing precarity and declining pay of PhD-wielding scholars, but less often do we recognize that even the most decorated intellectuals have needed immense extra-curricular scholarship and curiosity to maintain their academic relevance, and even this may not be enough. Noam Chomsky for example, regarded by many as the most widely-cited and useful academic intellectual of the 20th century (if not also 21st), developed his public career of advocating for “responsible intellectuals”, and by outwardly acknowledging the shallowness of his own authority as a technocratic mathematical linguistics professor. Since the 1950’s, the core of Chomsky’s academic program has been that of a positivist philosopher, a mathematical theorist of language. His self defense as a “responsible” intellectual was to take his credibility within the academic core of mathematical philosophers and extend his sphere of influence by first publicly proving his academic merit by lecturing on nearly all disciplines, and also critiquing mainstream institutions of public discourse. Which of course includes outlets like Fox News but also the academic institutions and their narratives as well. But linguistics departments today are not regularly hiring philosophers. They are hiring computer programmers. Chomsky’s podium has vanished, or is at least hidden to me in its current mutation (the radical ball is in your court, contemporary computational linguistics people).

Of course this is my primary defense of studying Economics, a field so financially rewarded by the neoliberal university while also seemingly essential to American ideology that it is hard to imagine a university without it. And yet, despite my networking efforts here in Oregon, there have been no expansions of humanities or social science faculty positions available for me. Here in Corvallis, the very STEM focused OSU is only expanding their data science department, which gave me a small opportunity to teach an empirically-driven Time Series course, but I couldn’t even snag a standard introductory statistics or probability course. For me personally, I’d prefer to teach economic theory and history of economic thought, which I will be able to do back in my teenage home of San Jose, CA. But for how long? When will my new university administration cut these courses as well? As we all know, it’s not like any form of scientific merit is going to uphold the existence of my Economics courses…

Studying, and even enjoying and succeeding in, math and statistics

So I more or less studied math as an undergrad. And while I recognize it is far from the only path, and almost surely not the best path, I would recommend it as a safe and potentially quite rewarding path to many who are interested in an honest scholarly career. And in that regard, I will conclude with some explanation of why I hold value in specifically proof-based mathematical education, which I was lucky to have.

Of course, even mathematics is under constant attack. As an undergrad I had the privilege of attending an American Mathematical Society conference, but disheartened when my genuine curiosity was ridiculed during a seminar on teaching statistics without probability theory, only computer programming and simulation. In hindsight, it might seem obvious that such a room would laugh at me seriously asking about the pedagogical loss of dismissing mathematical proof in the classroom, as this seems harmonious with the general trajectory of mathematical education within the neoliberal university system over the past decade.

Mathematical proof scares too many modern students if not also most technocratic professors. The idea that one can be engaged in mathematical scholarship without proof is itself preposterous, but socially understandable if we recognize the boogeyman of scary “proof-based math” as a useful force in producing highly credentialed “engineers” whose credibility is essentially defined by their willingness to “shut up and count”.

Data science scares me, but cutting mathematical proof from a technocratic education is an unnecessary danger that I cannot recommend to anyone. My advice to a burgeoning scholar is the opposite: to recognize employment within universities as a technocratic privilege, whose opportunities are dictated by changing educational demands. The changes are unpredictable, but this education system seems to be firmly founded upon tests of regurgitating mathematical objectivity.

Studying counting, studying math, is not something we should just shut up about. Proofs are the backbone of a valuable technocratic education, whether you are majoring in physics, economics, philosophy, or computer science. Proofs are not a test to determine if a scholar is innately worthy enough to view some clever form of divine mathematical nature. Rather, proof exercises, which must be graded by an instructor, teach students how to engage with the social struggle of “objective” numerical ideas. A logical proof is merely a collective agreement deduced from axioms of shared conception. In the case of a proof-based logic class, it is essential for the student to agree with the grader regarding principle axioms and laws of deduction. “Math” is the application of logical deductions to the realms of “numbers” and “sets”, a world imagined by believing that two distinct things can be the same as each other yet also different from everything else.

Statistics is similarly too socially important to just shut up about, or code about. Statistics is fundamentally a conception of probability theory, a view of a random world determined by independent fields of likelihood. All of data science is grounded in the laws of large numbers and central limit theorems, and thus some level of sufficient statistical analysis has become a barrier to entry in understanding the real weaknesses behind almost any form of empirical study. And while the statistical frameworks of independence can be wildly abused when blindly applied to the obviously interdependent realms of humanity let alone even the smallest cultures of microbiology, it is just as mentally abusive to deny the proliferation of smooth averages, bell-curve distributions, and the plethora of seemingly random and independent events found throughout our objectified worlds.

It is hard to understate the profundity that could be found in a basic study of math if we insist upon staying historical, dialectical, and recognize mathematical stories as stubborn foundational structures of the material culture that humans imaginatively perform. It’s an undeniably great place to start thinking about whatever the hell we seem to be talking about when looking out at this beautiful “objective” world.

But more importantly, to be crudely honest, I still feel being comfortable with mathematical proof is our safest bet to earn a paycheck from academies of data dominance and standardized hierarchical grading systems. So long as in-person hand-written exams are the most ChatGPT-proof form of student examination, hand-written mathematical proof remains a useful grading tool.

But we can’t let the brainwash sink in too deeply. We are bound between two delusional extremes. One is to say, “To hell with mathematics and its objective stratifications!”, while asking the university for scholarly privilege and denying one’s own use of mathematical discrimination to earn academic profit (grading). The other is to say, “Aha, I am so good at these institutional exams, I must have the best understanding of the world itself!”, deluding oneself into the various crude ontologies that insist math and probability define all existence. A critical and robust scholarly career needs some credibility in analyzing the history of the mathematical neoliberal university and its fundamentally discriminatory quantitative jargon. However, we cannot truly believe that playing the university’s games of numbers inherently grants us divine algebraic vision.

Upon embracing numerical analysis to seize the helm, sailing the choppy waters of truly critical historical scholarship is another journey entirely, one that we will have to navigate on our own. I am only suggesting that becoming comfortable with mathematical proof remains a pretty decent method for academic employment, as it grants scholars some legitimate credibility in the paradoxical yet proliferating task of objectively evaluating undergraduates: uploading homework assignments, writing exams, determining official grades, and preaching lectures.

Cheers,

Isaac