Progress in (a) Human Geography
I spent a lot of my undergraduate trying to whittle down haughtier edges of my supposedly sharp mind.
I am spending a lot of my graduate school experience being told those edges should be sharper. That my writing or my thinking has too many “could,” “may,” and “likely.” That I sell myself, my product, or my ideas short via qualifications and pervasive focus on the challenges my work faces or might face rather than the successes I may have at hand and or have had before.
It has been difficult for me to move back to those unqualified, strident statements of truth.
Certainty has become elusive for me. Academically, certainty is has always seemed distant because the best proof of optimality or convergence can have flaws or errors that the original author doesn’t see or understand. This lack of certainty is clearly related to imposter syndrome. common among graduate students, in a concern that everything you might do may secretly be premised on misunderstandings or inconsistent warrants. Compounding this, the approval-seeking calculus of graduate school can make it difficult to combat this concern.
At this point, I really will strive for formal results about the mathematical properties of models, computer systems, or theories. Since these results are sufficient (given their warrants), they’re really the only thing I feel completely sure about.
But, the warrants required can often be difficult to verify themselves. So, empiricists often resort to simulation-based justifications. But, these aren’t epistemologically sufficient. They confirm only that a proof hasn’t been demonstrated to be inconsistent out to some statistically-justified level of evidence. In this light, it makes sense that thorough sensitivity analysis, empirical use, and Monte Carlo experiments have become so much more common in quantitative social science. You need both the sufficiency of formal results and the necessity shown by empirical backing to convince people. When formal results are elusive or difficult to ground because they require strong warrants and assumptions, evidence that a procedure results in empirically reasonable estimates becomes important.
What’s a bummer is that this positive skepticism I hold so strongly shows up in statements I make about my work. On a bad day, this can drive doubt in others. I know I would feel dodgy about a claim whose author “hasn’t yet found a reason why this proof/derivation is wrong.”
This epistemological struggle, which might also be conflated with having confidence in your work, is one I definitely have not figured out quite yet.
What I do know is that, for me to be confident in my work, I need to work hard to verify formal results about a model, program, or problem. Then, I have to set rigorous bounds on what “counts” as necessary empirical evidence. It’s much more persuasive to me when these bounds are “preregistered,” and I’ve set them up after the formal results but before the computations.
This was, in part, why I started this blog, before GSOC co-opted it, so it might return that direction.
What remains a challenge for me, though, is to speak confidently. While I may simply be too skeptical of both myself and others, finding sure footing on your research agenda is clearly critical for success, and it’s also connected to many components of a successful academic career.
Hopefully, I’ll find that certainty sooner than later. Maybe this means science is still too numinous for me. Maybe this epistemological concern will end as I get older. Or, maybe, no one else has this figured out either. I’m not certain :)
imported from: yetanothergeographer