Just a brief note on something (one more thing) I don’t understand about academic publishing.
Nobody has ever explained to me in detail what a journal expects from me as a reviewer. Nobody has explained to me what a review exactly must be. I just pretend to go along with a tradition everybody seems to know fairly well although it’s clear nobody really does. This sounds pretty absurd but it’s quite common in academia.
The blurred idea we all have about peer-review is that few colleagues with a fair knowledge of your subject of interest give some unreplicable (so, statistically speaking, random) opinions about your work so that you can improve it (hopefully) and they can help the editors to decide whether your article is good enough to be published in their journal.
This last bit is the one that bugs me: I’m sorry, I can’t say whether an article is good/important enough for the standards of your journal. I don’t think it’s fair that professional careers depend at some extend on my arbitrary opinion as a reviewer (since we all care so much about individual publication records) and I won’t damage anybody (even if it’s only a little bit) based on my particular taste. And even if I’m sure I’m right, I don’t want to act as a gatekeeper: I want different perspectives to be included, not only those I like.
That doesn’t change the fact that you can be exigent: if there’s something false, you can say you think it’s false. If conclusions can’t be deduced from the results, you can also say so. But that doesn’t change the fact that 1) maybe you’re wrong, 2) authors could always change what you think it’s wrong or incomplete, 3) maybe the article is valuable for some other reason. And even if you think the article is horrible, remember it is probably going to be published elsewhere anyway.
Then, yes, controversial articles may be considered at the same level than more robust articles, but isn’t it the case already? And for that I blame the fact that the journals don’t publish the reviews along with the articles (which makes the review process less transparent and brings all the known problems opaque procedures are known for).
So, my policy as a reviewer is: accepting to review only for society journals or non-profit organizations, writing lengthy reviews that give as much information as possible to the authors and finally, unless there’s something grossly wrong (i. e. the suspicion of some dishonest behaviour), I’m accepting the article.
Lately, I’ve started to learn some machine learning. As an intro, I’ve completed one of the coursera MOOCs devoted to that, in particular the Machine Learning course designed by Stanford University. I find MOOCs super useful introductory courses. It was a nice experience and I learnt many things I didn’t expect to learn. In first place: Octave. As Blas Benito told me on Twitter, Octave is a fully-developed programming language ‘compatible’ with Matlab (I grossly labelled it as a ‘open-source version of Matlab’). I worked a bit with Matlab during my PhD but then I abandoned all my scripts because it isn’t free, I wish I had known back then Octave. Anyway, the whole Machine Learning course is developed with scripts and exercises in Octave (which is quite intuitive if you already know R).
Now, to better integrate the exercises and just in case I want to use some of the methods in my future research, I’ve translated all the exercises and scripts to R. You can find them in my Gitlab site. Over the next few weeks I hope I’ll get to translate them to Python too, as this could help me to catch-up with Python. In the near future I’d also like to develop these scripts to accommodate multivariate data as dependent values (maybe at the same time I write the Python functions).
One of the things that really got my attention during this course was the first lesson, on linear regression. I already knew how to run a linear regression from scratch and to estimate analytically the least-squares linear function, with its slope and intercept. When I saw they were explaining an iterative process to obtain the best-fitting linear function I got the impression that they were using an unnecessary complicated process based on brute-force principles to estimate something pretty simple. When things got complicated later, I understood that this was a necessary introduction for more complex cases.
I still think mathematicians and statisticians would be puzzled by the long procedures used by data scientists, while computer scientists might be amazed by the efficiency of such a long array of algorithms ran on high-dimensional data. At the same time, I don’t think mathematicians and statisticians have a clear answer for many of the questions where high-dimensional data is involved while computer scientists, even if by unsophisticated means, have found their way into largely unexplored areas with reasonable efficiency. Maybe trying to figure out an analytical way of explaining the algorithmic results would be the golden ticket. Meanwhile, I’ll stick to algorithms whenever I need them but there’s no way I’ll abandon normal equations for linear regression.
Today, navigating among the old files in my computer, I found a text and some figures in which I tried to explain why comparative methods might be of interest for people studying macroeconomy. I just applied the same reasoning we usually do in biology: countries are not independent entities since historically they’ve had different degrees of relationships (some have arisen from some others). Therefore, the type of relationship among macroeconomic variables we usually see in graphs with a large number of countries might only reflect a random correlation product of the historical relationship among countries. I know, this kind of metaphors biology-economy (or evolution-history) are flawed and, as one of my former bosses put it once: ‘the danger of metaphors is that the better they are the less they look like a metaphor’.
That’s probably why it was among old files in my computer, where this kind of fast idea should be, and I’m happy to say that. However, because this blog is a bit of a jumble too, I thought I’d share the graphs here: