Academic Productivity

Nick Cohen has a provocatively titled piece in today’s Observer, “No one wins in modern-day academia”, examining the shortcomings of the Research Assessment Exercise.

If you’re a UK academic, you’ll know all about the RAE which, as it says on the tin, is an exercise to assess the quality of a department’s research and consequently determine future levels of government funding. My experience with this so far has been quite limited. In my old department, we measured success through policy influence, not journal publications, and my current work is just starting to yield results. But even if you are an active participant in the RAE, the question still remains: is this really the best way to assess our overall effectiveness and success? Academic life is about more than just research: we are also teachers, administrators and professional community members.

So setting aside the official funding role of assessment for the moment, I’d like to ask an open question. How do you evaluate your own success as an academic? (And as a relevant corollary, how does this affect how you choose to spend your time?)

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In the last month or so (sorry, we haven’t posted in a month!) I’ve been reading on and thinking about attention economy. I think it is the right paradigm to connect the different bits and pieces of productivity knowledge (we could call them hacks) floating around on the ‘net.

I could write a long intro to the attention economy ideas and how they affect the way we process information AND make decisions… but I have written a series of 4 posts on attention economy and I’d better redirect you there. So, ideally, before you continue reading this post you should have at least skimmed that series, and you should be comfortable with it.

The question I want to address on this post is this: Are we rational about how we allocate attention? This is an important topic because attention allocation to different scientific topics can make or break your career.

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Good writing is a skill. I’m not saying I have it, and remember, this is a blog post, maybe the fastest form of writing and reading ). As a skill, it requires practice. And, as Graham says, “Writing doesn’t just communicate ideas; it generates them. If you’re bad at writing and don’t like to do it, you’ll miss out on most of the ideas writing would have generated.”In fact, writing posts like this one is helping me to review and polish ideas I didn’t know I had about writing till this very moment. I’ll start with the most obvious, and will get more subtle/interesting as the list grows.

1. Get your relevant Manual of Style. (e.g., Chicago/APA). APA wants you to buy it in book form, but I think this is one of the resources that should be online.
2. Get Oliver Strunk’s elements of style. It’ll recommend some rules of thumb that may well be obvious (e.g., avoid passive voice. Reduce the use of adverbs to a minimum) but overlooked. There have been several editions, and the older ones can even be found online.

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Disclaimer: this post may be relevant only for social sciences/psychology people. I found a nice thread on the Judgment and decision making (JDM) mailing list on the need for replications.

Lots of good posts on an interesting discussion. The mainstream view is that we simply don’t run enough replications because they are harder to get published. This leads to studies showing that replications are actually very hard, with only a small percentage (about 40% in the social sciences) being successful.  Robyn Dawes seems to thing that replications are overrated:

the “real” scientists do is to futch around until they get it “right.” The multiple study requirement just adds “first and second and third” studies, thereby wasting space and time.

There are comments on Increasing the Percentage of Papers Replicated, and some nice book recommendations on experimenter bias.

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Today, while googling for “tenure rat race”, I found Jonathan I. Katz’s page: “Don’t Become a Scientist!“. I find his honesty devastating:

Are you thinking of becoming a scientist? Do you want to uncover the mysteries of nature, perform experiments or carry out calculations to learn how the world works? Forget it!

Science is fun and exciting. The thrill of discovery is unique. If you are smart, ambitious and hard working you should major in science as an undergraduate. But that is as far as you should take it. After graduation, you will have to deal with the real world. That means that you should not even consider going to graduate school in science. Do something else instead: medical school, law school, computers or engineering, or something else which appeals to you.

Why am I (a tenured professor of physics) trying to discourage you from following a career path which was successful for me? Because times have changed (I received my Ph.D. in 1973, and tenure in 1976). American science no longer offers a reasonable career path. If you go to graduate school in science it is in the expectation of spending your working life doing scientific research, using your ingenuity and curiosity to solve important and interesting problems. You will almost certainly be disappointed, probably when it is too late to choose another career.

I think he is right in many levels. But let’s concentrate just on the simplest, easiest to measure: money.

If we academics do the computations proposed in Figuring Out Exactly How Much Your Time Is Worth [The Simple Dollar], we may be in for a surprise.

Basically, you determine your true hourly wage by subtracting all of your work-related expenses from your salary, then calculating the hours you devote to work each year (including commute and other time-sinks) and dividing your remaining salary by your total hours.

Since we work silly hours, the actual pay is quite ridiculous. Of course, one has to factor in the liberty to think, flexible hours etc.

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Are academics managing projects? The thesis of this post is that we academics are project managers without formal training in project management. You ask for money to do a research _project_. If you supervise or mentor students until they get their PhD, you are managing a project. If you teach a class, you are managing a project. Do you see where I’m going?

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The November 2006 issue of PLoS Computational Biology has a short editorial with ten rules for evaluating postdoc opportunities. An interesting — albeit commonsensical — collection of hints, if you’re approaching the end of your PhD and looking for job opportunities after your defense.

Ten Simple Rules for Selecting a Postdoctoral Position

Thanks Benoît for the pointer.

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Internet Marketers (IMs) have an advantage over other professions: they have pretty detailed statistics to use as feedback. For example, they have as indicators hits, time between buys, length of their customer lists, and ultimately… the money they make! They check these statistics daily.

Musicians are punished horribly when they fail performing a passage, not only by their peers but when practicing alone, by their own musical sense jumping in disgust!

In other professions, for example academics, we don’t get such a direct feedback. We may get feedback by how many papers we get published a year, but this is too coarse of a measure, and it only comes in yearly.

We may also consider our rate of success getting funding, but this is again a coarse measure, since we apply to at most dozens of grants in a lifetime.

In teaching, we may get a more direct feedback in that students are normally very expressive and their faces reflect how well our current lecture is doing. Yearly evaluations are also evidence of our performance. But nothing this immediate and direct is available when, say, you are writing a paper.

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This is one more post on the Hamming series about how to select your research career topics.

It takes courage to think about important unsolved problems. (Excepting of course the officially canonized problems, such as Hilbert’s, Fermat’s Last Theorem, P = NP, …). But the solutions that made a difference were to problems that were not even recognized as such!

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Today my post will be a bit more high-level than usual. Most of us select scientific topics without paying much attention to overall strategy (i.e., which ones may produce the most benefit).

On this, the best piece of writing I have found is Richard Hamming’s famous essay “You and Your Research” (which is a transcription of a talk he gave at bell labs, e.g., here, and here), Richard Hamming suggests that you ask yourself three questions:

1. What are the most important problems in your field?
2. Are you working on one of them?
3. Why not?

“If you do not work on an important problem, it’s unlikely you’ll do important work. It’s perfectly obvious. Great scientists have thought through, in a careful way, a number of important problems in their field, and they keep an eye on wondering how to attack them.”

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