Academic Productivity

Cambridge zoologist Peter A. Lawrence has published a thoughtful piece on the frustration of scientists (whether young or not so young) facing the ruthlessness of the research granting system (Real Lives and White Lies in the Funding of Scientific Research). He suggests how a “drastic simplification of this grant-writing process would help scientists return to the business of doing science” and quotes a passage from a recent NYT column by Stephen Quake, who asks what sounds to me like a challenging question:

Could we stimulate more discovery and creativity if more scientists had…security of…research support? Would this encourage risk-taking and lead to an overall improvement in the quality of science?

I take this as a genuine question in search of a convincing empirical answer.

• The full article is available in PLoS Biology.
• CC-licensed photo courtesy of A. Kuzminski.

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?)

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.