‘This is why I don’t think his explanation is effective, ultimately, why I think [Sherman Alexie] made the wrong decision. Everyone thinks he made the wrong decision, pretty much; even he seems to think he made the wrong decision. But he couldn’t find a way to make the right one. Which is why the problem is not that a cynical and dishonest person gamed the system. The problem is that the system is already rigged, and there’s no fixing it.
At best, all canons are a necessary evil. They serve a purpose, and sometimes it’s a good one; most of the time, I would suggest, they do more harm than good. But even if we grant, for the sake of argument, that they are sometimes necessary, any list of “best of X literature” is always going to be a better device for exclusion than for adequately representing anything but the preferences of the gatekeepers. “Representation” is always subjective and arbitrary, because it’s always, also, a synonym for simplification. The map is not the territory, and the Best is not American poetry. You can aspire to produce a list that doesn’t radically misrepresent the field of whatever it is you’re trying to survey, and this is what Alexie tried very hard to do; at best, your omissions and failures might not be glaring. But that’s it, that’s the best case scenario, to fail not so badly. There’s no target to hit, here: “representation” is a mirage, because all representations are, in crucial ways, also un-representative.
It’s a huge problem that “best of” lists are mostly white males, of course, and any variation thereof. It’s a form of violence. But that’s precisely why we should not take such lists seriously. Majority groups tend to dominate “best of list,” because that’s what “best of” lists are good for. They are excellent instruments for naturalizing exclusion. It’s no surprise, then, that white males tend to really be invested in those lists, and in shibboleths like “maintaining standards.” A “best of” list creates the appearance of level playing field, by investing in the conceit that everyone’s work could be judged by the same standard—that such a thing is even possible—and, so, the entire enterprise gives ideological cover to those who would like to believe their work has been praised on its merits (and that those who have been excluded, in some sense, deserved it). But a representative “best of” list is not something to strive for, because it’s a contradiction in terms. There is no level playing field, and never has been. Because canons of all kinds presume one, they tend to make the problem worse.
In short, the fact that canons are instruments for exclusion is not a reason to try to fix them: it is a reason to abandon them.
Here’s an alternative: what if we’re going about this all wrong? Maybe the best way to get to the promised land of objective judgement is a data-driven statistical accounting of each poem’s strengths and weaknesses, rigorously compiled by a standardized methodology. We need solid models—so a great deal of study is called for—and we need to think hard about how to find more accurate methods of evaluation. Crowd sourcing is a start, but it won’t be enough; feelings and emotions might creep in there, along with the pesky notion that cynicism and dishonesty are somehow incompatible with poetic beauty. Only will truly high-powered computing be able to sort through such noise, I think. So bring on the algorithms. Big Data will save poetry.’