Week 2: Nuance vs. Pragmatism

This week’s readings give us a great jumping off point for why interdisciplinary classes like this are necessary and should be embraced by academia. In my mind, the essays we read boil down to cross disciplinary communication and issues of close and distant approaches, or micro vs. macro, or nuanced vs. pragmatic if you prefer.

Dr. El Abbadi’s piece was great for a humanist like me to get insight into how a computer scientist views data or a problem, particularly in terms of wanting the most efficient (and helpful to the user) solution, and not just merely a solution. This is a problem that I think occurs in much of the humanities, where we get lost in high-minded concepts that alienate our work from others, be they in a different discipline or completely outside of academia, or even from the very classroom in which we are trying to transmit the information over to the undergraduate student population. We’re good talkers, and can certainly elucidate a topic or bring up important issues, but sometimes the talk leads to very little impact for the “user” or student for that matter.

But both Manovich and the Striphas provide a glimpse into a more complicated approach that marries both the pragmatic and efficient computational methods with the critical humanist approach. Rather than battle with each other like it’s the good ol’ days of academics, building our reputation off of esoteric assaults at other over-educated intellectuals in our privileged bubble, underlying both pieces is that collaboration is the key. Tracing the history of data like Striphas and Roberston/Travaglia provides vital insight into the consequences of data analytics, and how we might learn from past mistakes in order to build a more complicated portrait from any given dataset.

I’ve read the Robertson/Travaglia and Manovich pieces previously, and there is certainly more to say about both (if I’m going to get all critical humanist), but again the most important takeaways from the readings this week were collaboration and complexity. It’s why Manovich proposes the “wide” approach to data, and why he stresses the overlap of so many different projects.

Whether you call them Digital Humanities, Cultural Analytics, or Social Computing, there will be people dissecting your methods to shreds. There will be attacks and superficial dismissals and even blind support of these new paths forward, sometimes labeling them as trends or money-grabs. In my own program, whenever I mention the Digital Humanities I get one of two reactions: (1) utter contempt and indignant comments about distant reading, without really understanding the idea, or (2) “oh that’s really big right now, it’ll help you get a job.” Maybe it will (crossing my fingers), maybe it will help me a get a big fat grant like Manovich, but more importantly, as it relates to my research interests as well, I hope this interdisciplinary approach brings positive gains for everyone— people in a variety of disciplines in academia and (especially) those without the privilege to be involved in these discussions.

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