You're probably dead [Tania, Core Post #2]

It might be bad form to fixate on small parts of an essay rather than the whole; it might make us miss the big picture or lead to conclusions drawn from flawed data sets, to go with the theme. Good scholarly form might encourage us to go beyond past single-sentence analyses, to keep irritation in check and keep reading. But I'd like to inhabit bad scholarly form for the space of this post to marvel at the sentence: "And hundreds of civilians have since died as a probable result" (Cheney-Lipold, 40)?

What should we make of that "probable"? Cheney-Lipold is writing here about drone casualties in the wake of the U.S's move from targeted to precision/signature strikes aimed not at people but at "patterns in data." The implication is that in the absence of sufficiently accurate data on such attacks and subsequent casualties, Cheney-Lipold must—for the sake of good form—hedge his estimate, must mark for us his awareness of the status of the figure "hundreds of civilians" as unverifiable. Merely probable. 

This unverifiability—the fact that a byzantine bureaucracy suppresses transparency in the matter, leaving it to myriad researchers and journalists to try to assemble a picture (e.g. The Bureau of Investigative Journalism)—is more than incidental. It is a war tactic, the flip side of claims to precision. If the U.S. has billed its more recent drone strikes as “precision” strikes (as Cheney-Lipold notes), this is enabled precisely by the fuzzy accounting that attends it. In this context, passing references such as the author’s own (“And hundreds of civilians have since died as a probable result”) serve to normalize the criminal fuzziness. The passing sentence, like the glance or cinema’s unmarked B-roll, constructs (and not merely reflects) a vision of the world; they fix their objects as transparent, undeserving of further attention. What can be mentioned in passing can be taken for granted.

And this is a function perhaps not unrelated to, even if formally distinct from, the types of world-modelings that this week’s texts concerned themselves with. Before we turn to analyzing big-data-fueled predictive algorithms, it might serve us well to consider our own rhetorical practices (not to put too fine a point on it—though if I’m to fully inhabit bad form, I might as well.)