Last week’s reading assignment for class included an unexpectedly entertaining essay: “The Defense of Poesy” by Sir Philip Sidney.  Sir Philip starts out with a thing that’s certain to win me over:  He runs down a list of the reasons people take up the study of science, music, mathematics, etc. … and he gets them right.  It’s not to be taken for granted that a poet will understand that.  But then we get to the fun parts.

First thing I loved:  Sidney spends a paragraph denouncing people who write with “painted affectation”, among whose sins are using too much alliteration (“coursing of a letter,” he calls it).  And the very next sentence he writes has six “p”-words in it!

But I would this fault were only peculiar to versifiers, and had not as large possession among prose-printers, and, which is to be marveled, among many scholars, and, which is to be pitied, among some preachers.

Second thing: He makes his case, and then finishes up the essay with a curse on anyone who doesn’t believe him.

…if you have so earth-creeping a mind that it cannot lift itself up to look to the sky of poetry, … thus much curse I must send you in the behalf of all poets:  while you live in love, and never get favor for lacking skill of a sonnet; and when you die, your memory die from the earth for want of an epitaph.

I think everyone should do this.   A curse on the unconvinced should be a formal part of academic writing, like a warrior’s boast in Anglo-Saxon, or invoking a Muse at the start of classical poems, or mentioning a season in a haiku.

But that’s not what this post is about.  This post is about Oscar Wilde’s famous epigram, “We are all in the gutter; some of us are looking at the stars.”  It turns out that I never understood that line. I was thinking of the gutter as where we habitually spend our time (“get your mind out of the gutter”).  Now I know that Wilde pinched that image from Sir Philip, who wrote, “But when by the balance of experience it was found that the astronomer, looking to the stars, might fall into a ditch …”.   Yeah, I’ve done that.  Of all the images used in Elizabethan literature, this one might have gained the most relevance for the XXI Century.  Were he writing today, Wilde might rather have said,

We are all in the gutter, but most of us were looking at our phones.

I am back in an English class, for the first time since 1979.  Signum University is running a class called “Research Methods”.  I signed up because I’m old. Two years ago I discovered that, although I was a state-of-the-art statistician in 1982, the things I know don’t count as knowing statistics any more.  The same thing may have happened here. And so it appears. Half the syllabus sounds like the first month or two of this blog. (Good – I’m not doing it wrong!) The other half is things I’ve never even thought of. (Better!)

One of the books they’re making us read is called The Craft of Research.  I like the word “craft” there. Research is not a science [1], and it would be pretentious to call it an art. It’s something in between. It’s an excellent book in almost all ways. My reactions to it alternated among “obviously – what else would one do?”; “have you been looking over my shoulder?”; and “wait – I thought I invented that!”  But there’s one point with which I must take issue.

Chapter 3 is an orc’s breakfast. Their guidance about doing research that doesn’t make people ask,”so what?” is to think on three levels:

1. I am studying x,
2. Because I want to find out y(x),
3. Which will help the reader understand Important Thing z, of which y is an element.

They talk as if you do research by starting with your source of data.  I would have had no objection to this formulation in the 20th Century.  Now, though, this is the canonical drunk looking for his keys under the lamppost. In the age of Cheap Data it has become a trap.

Digression

Most people who like to talk about the leading edge of technical progress say “big data”, and justify its importance by telling stories of google searches and flu outbreaks. But when you ask them the most basic question, “How big is it?”, you find that they aren’t all talking about the same thing. There’s one definition I actually like: “Big data is big enough that it won’t fit on a single machine — which means you need to use specialized tools to muck with it.”  Readers of this blog know how much I like Wikipedia, but in this case they let me down: “Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.” (They then go on to list the same jobs everybody has ever had with collecting measurements of any kind.)  People who sell storage and processing power like to brag that what you’re thinking of won’t challenge their machines.  I have a certain affection for the smartass response:  “If you have to ask this question, your amount of data isn’t that big 🙂 …”.  But there’s no way to argue that the term is well defined.  That’s why, instead, I say “cheap data”.  That’s what it really is.  Anyone who’s ever assembled a large set of measurements by hand knows exactly what I mean.

End Digression

The world is now full of databases.  I work with dozens of people who build and maintain them.  For them, Step 1 is a given.  They’re studying their database because that’s what they do.  Why anyone should care is above their pay grade.  When I’m a reviewer, I get papers with this mistake in them all the time.  (It does not go well for the authors’ major professors.)

To avoid the seductions of databases [2], the sequence ought to go:

1. Thing z is important, and readers will understand it better if they know y.
2. Thing y is a function of x, which is accessible through means I’m good at,
3. So I’m studying x, and here’s what I found.

I don’t obey this structure with perfect fidelity.  This post and this one are pretty much of the form, “I’ve got a database and nobody can stop me from using it.”  That’s OK for a blog (in moderation) because this is a place for scintillating insights, wild-goose chases, and things that turn out to be dumb, without discrimination on the basis of merit.  But mostly I’ve stuck to my preferred structure.  And if the rest of the world doesn’t come along with me, well, let a hundred flowers bloom; our papers won’t all sound the same.

[1] Academic disciplines with the word “science” in their names aren’t sciences. Nobody ever studied in a department of Chemistry Science, or Mathematics Science.

[2] Google assures me that phrase exists nowhere but here at Idiosophy.

Brad DeLong has an extraordinary economics blog. You can find there lots of economic history, center-left politics, and also unique compositions like a Socratic dialog concerning the Greek fiscal crisis, jokes about the Valar, and who knows what else.

This weekend, he posted about the (lack of) philosophical foundations of quantum mechanics. [1] That’s an interesting topic in itself, but what struck me was the fact that he was quoting from an optically-scanned PDF of a magazine article by John Bell.  The OCR program wasn’t invented for physics, so when we do things like stick a greek letter into an english sentence, it gets confused. But here’s the thing. It wasn’t hard to read.  Because I’ve been through the arguments a dozen times before, it was easy to figure out that”If we take advantage of the indistinguishability of p and p…” actually has two ρ’s in it, and one of them has a circumflex over it.  (By symmetry of the copulating conjunction, it doesn’t matter which one. [That’s a physics joke])

When I look at a medieval document, I can usually recognize a bunch of the words. But understanding full sentences isn’t as easy, so I find a paper by a medieval scholar who interprets it. How do I know she got it right?  This little exercise in point-of-view reversal has given me a lot more confidence that they know what they’re talking about.

[1] If you’re wondering why, a speculation: Prof. DeLong frequently discusses the problems with dynamic stochastic general-equilibrium models of the macroeconomy. This may be a case where he’s looking at physics for guidance about how to stay connected to reality when your equations are fiendishly complicated.