It turns out it takes two weeks to recover from
hosting a conference, but that time allows you to ponder the fate of the world.*
Particularly, I’ve been wondering what Nate Silver means for rhetorical
studies, and given what Nate Silver meant for baseball analysis, this is also a
story about negotiating the relationship with my inner curmudgeon.
The quick recap of Nate Silver:
He developed an insightful way of measuring baseball success, was derided by crusty old scouts and baseball writers for failing to appreciate the art of the game (especially the art of stolen bases, which are statistically a terrible bet and so seldom happen in today’s game**).
He developed an insightful way of measuring electoral success, was derided by crusty old pundits and political writers for failing to appreciate the human agency of retail politics.
My recap of Silver, of course, indicates where my discomfort rests in his analytic process. It’s a question of art versus science, of human contingencies versus systematic predictabilities. These aren’t always or necessarily zero-sum arrangements, and that’s what makes Silver so fascinating and maddening to me; he’s likely the best locus for negotiating how rhetoric manages these conditions in practice.
Here’s what election night looked like on my Twitter feed:
Me: Given the developments in electoral strategy and of provisional balloting, is the new normal: a week-long count? #rhetoric of the in-between?
Colleague 1: found it funny today that my humanist colleagues put all much faith in Silver/Cohn et al, ie, the sabermetricians of politics
Me: Silver is weird for our business. Colleague 2 frames him via facts, but Silver's doing something to our sense of contingency, no?
Colleague 1: Agreed. Not just in the framing of political battles, but in the sense of how we might even begin to respond.
Colleague 2: Silver et.al. give us probabilities--Aristotle's contingency at least as T. Farrell understood it.
My initial concern with Silver, on election night, was that his system took the element of surprise out of the evening, and out of the lead up to the election. If Silver’s system is about probabilities as a tactic for managing contingency, I’m not sure what work is left for rhetoric. Of course, that puts rhetoric on the side of talking heads and pundits, and I’m not sure we want to be in that position either. John Murphy does a nice job discussing the lessons we might learn from this election, and a notion that Silver has shifted how we value rhetoric in campaigns.
The day after the election, a colleague in the English Department tried to convince me that I could embrace Silver’s data-centric approach to politics, since rhetoric drives the poll numbers. That might be one way to identify rhetoric’s importance in this autopsy, but I’m not sure I’m comfortable situating rhetoric alongside focus groups and targeted media campaigns. Surely, rhetoric is at play there, but I’m not sure we want it to exist only there.
It occurs to me, as I work through this issue in this space, that my problem with Silver isn’t a problem about what Silver means for rhetoric: I’m really not interested in the disciplinary navel gazing about whether Silver necessitates we all become economists or statisticians to manage contingency or if we are relegated to constructing discourses addressed to polling probabilities. Either of those responses would be a useful addition to our repertoire to some extent. What concerns me about Silver is what rhetoric means in the language of Sabermetrics.
This is where Colleague 1’s note becomes significant. Silver’s analytics came to frame the discussion of the election in its penultimate week, and it’s unclear how we engage that framework rhetorically. The pundit-class reacted by dismissing it. Peggy Noonan invoked the affective dimension stating that she “felt” the election differently than the polling data and put in a great Cartesian example of the deception of human perception when she wondered “Is it possible this whole thing is playing out before our eyes and we’re not really noticing because we’re too busy looking at data on paper instead of what’s in front of us?” But in terms of rhetorical studies, the question has to be how we negotiate the relationship between the contingent language of probability and the civic language of judgment.
He developed an insightful way of measuring baseball success, was derided by crusty old scouts and baseball writers for failing to appreciate the art of the game (especially the art of stolen bases, which are statistically a terrible bet and so seldom happen in today’s game**).
He developed an insightful way of measuring electoral success, was derided by crusty old pundits and political writers for failing to appreciate the human agency of retail politics.
My recap of Silver, of course, indicates where my discomfort rests in his analytic process. It’s a question of art versus science, of human contingencies versus systematic predictabilities. These aren’t always or necessarily zero-sum arrangements, and that’s what makes Silver so fascinating and maddening to me; he’s likely the best locus for negotiating how rhetoric manages these conditions in practice.
Here’s what election night looked like on my Twitter feed:
Me: Given the developments in electoral strategy and of provisional balloting, is the new normal: a week-long count? #rhetoric of the in-between?
Colleague 1: found it funny today that my humanist colleagues put all much faith in Silver/Cohn et al, ie, the sabermetricians of politics
Me: Silver is weird for our business. Colleague 2 frames him via facts, but Silver's doing something to our sense of contingency, no?
Colleague 1: Agreed. Not just in the framing of political battles, but in the sense of how we might even begin to respond.
Colleague 2: Silver et.al. give us probabilities--Aristotle's contingency at least as T. Farrell understood it.
My initial concern with Silver, on election night, was that his system took the element of surprise out of the evening, and out of the lead up to the election. If Silver’s system is about probabilities as a tactic for managing contingency, I’m not sure what work is left for rhetoric. Of course, that puts rhetoric on the side of talking heads and pundits, and I’m not sure we want to be in that position either. John Murphy does a nice job discussing the lessons we might learn from this election, and a notion that Silver has shifted how we value rhetoric in campaigns.
The day after the election, a colleague in the English Department tried to convince me that I could embrace Silver’s data-centric approach to politics, since rhetoric drives the poll numbers. That might be one way to identify rhetoric’s importance in this autopsy, but I’m not sure I’m comfortable situating rhetoric alongside focus groups and targeted media campaigns. Surely, rhetoric is at play there, but I’m not sure we want it to exist only there.
It occurs to me, as I work through this issue in this space, that my problem with Silver isn’t a problem about what Silver means for rhetoric: I’m really not interested in the disciplinary navel gazing about whether Silver necessitates we all become economists or statisticians to manage contingency or if we are relegated to constructing discourses addressed to polling probabilities. Either of those responses would be a useful addition to our repertoire to some extent. What concerns me about Silver is what rhetoric means in the language of Sabermetrics.
This is where Colleague 1’s note becomes significant. Silver’s analytics came to frame the discussion of the election in its penultimate week, and it’s unclear how we engage that framework rhetorically. The pundit-class reacted by dismissing it. Peggy Noonan invoked the affective dimension stating that she “felt” the election differently than the polling data and put in a great Cartesian example of the deception of human perception when she wondered “Is it possible this whole thing is playing out before our eyes and we’re not really noticing because we’re too busy looking at data on paper instead of what’s in front of us?” But in terms of rhetorical studies, the question has to be how we negotiate the relationship between the contingent language of probability and the civic language of judgment.
Of course, a true October surprise would undercut
this approach to contingency, but then, we’d have to live in a world where the
stability of campaigning openly courted the instability of surprise. This might
be my most clear assertion about Silver’s relation to rhetoric. Much like we
bemoan teaching to the test in pedagogical terms, I worry that we position
ourselves as discoursing to the poll in statistical terms.
Here we return to the old, tea-leaf reading scouts that dismissed Silver in the realm of baseball. No matter how good a player looks on the field, Silver’s metrics could predict success consistently. No matter how folks felt about Romney, 9 times out of 10, he was going to lose the election. That’s not to say he couldn’t win, but if it were a table game in Vegas, the odds would be long. With Silver providing that level of stability to the probabilities, the contingencies of fact (in Colleague 2’s statement) are no longer spaces for fabricating artistic proofs. If the spaces for constructing such proofs are in ensuring an adequate data pool for managing probabilities,*** then we are best served not by topoi for framing probabilities but by gathering and analyzing the metrics of the audience. And how do we teach the audience to engage and circulate those metrics? What are the odds we can teach people to deliberate the odds? And how do we manage that emerging field of rhetorical resource against the affective dimensions that work (at times, at least) counter to them?
Obviously, I’m using this space to work through my own inability to articulate why Silver makes me nervous. It isn’t a damn-the-math mentality. I don’t want to be the luddite or the cranky baseball scout. Better living through science, say I. But given the effectiveness of Silver, and the fact that technological shifts make gathering and aggregating collective data on nearly every facet of everyday life efficient and scalable, I’m not sure what the rhetorical terrain looks like in a Sabermetric world.
*When there happens to be a general election during your recovery period.
Here we return to the old, tea-leaf reading scouts that dismissed Silver in the realm of baseball. No matter how good a player looks on the field, Silver’s metrics could predict success consistently. No matter how folks felt about Romney, 9 times out of 10, he was going to lose the election. That’s not to say he couldn’t win, but if it were a table game in Vegas, the odds would be long. With Silver providing that level of stability to the probabilities, the contingencies of fact (in Colleague 2’s statement) are no longer spaces for fabricating artistic proofs. If the spaces for constructing such proofs are in ensuring an adequate data pool for managing probabilities,*** then we are best served not by topoi for framing probabilities but by gathering and analyzing the metrics of the audience. And how do we teach the audience to engage and circulate those metrics? What are the odds we can teach people to deliberate the odds? And how do we manage that emerging field of rhetorical resource against the affective dimensions that work (at times, at least) counter to them?
Obviously, I’m using this space to work through my own inability to articulate why Silver makes me nervous. It isn’t a damn-the-math mentality. I don’t want to be the luddite or the cranky baseball scout. Better living through science, say I. But given the effectiveness of Silver, and the fact that technological shifts make gathering and aggregating collective data on nearly every facet of everyday life efficient and scalable, I’m not sure what the rhetorical terrain looks like in a Sabermetric world.
*When there happens to be a general election during your recovery period.
** Unless you’re a Taco Bell fan, in which case stolen bases are a good bet for a free taco during the World Series.
***Silver missed only two Senate races. Those in Montana and North Dakota, where the stakes for polling were low and so yielding a very small (and compromised) data pool to inform his probabilities.
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