Posts tagged with academia

From commensurability to commensuration is a long trek, and we should feel self-congratulatory at this juncture.

Historic events have turned [our] guild from theory toward—if not practice, then at least talk of practice.

Research focuses on real wages—wages that are adjusted for inflation. Getting data on wages is tricky. But accounting for inflation is even harder. (For example, workers often paid rent informally, meaning that there are few records around).


And so it is unsurprising that researchers differ in their estimations of real wages. Some, such as Peter Lindert and Jeffrey Williamson, suggest that full-time earnings for British common labourers, adjusted for inflation, more than doubled in the seventy years after 1780. But Charles Feinstein argued that over the same period, British real wages only increased by around 30%. It’s a bit of a … mess.

Most people agree that after about 1840, real wages did better. Nicholas Crafts and Terence Mills shows that from 1840 to 1910, real wages more than doubled. Their findings are mirrored by other researchers ….


in almost all British cities, mortality conditions in the 1860s were no better—and were often worse—than in the 1850s. In Liverpool in the 1860s, the life expectancy fell to an astonishing 25 years. It was not until the two subsequent decades that rises in life expectancy were found



Over a year ago, I wrote a letter to the editor of the Journal of Computational Sciences, urging the retraction of Bollen, Mao, and Zeng’s paper, “Twitter Mood Predicts the Stock Market.” Since JoCS is an Elsevier journal, one does not simply email the editor.
Rather, one has to register with the Elsevier author system, … submit LaTeX source code of a letter, along with supporting documents, author bio, .… I distilled the main arguments into two:

  1. first, that the Granger causality tests presented in BMZ’s paper are … datamining, and present no evidence for a connection between Twitter and the Dow Jones Index;
  2. and that the quoted predictive accuracy of the forecast model is so high, it would … [contradict] the experiences of … [traders] … and so this forecast accuracy is likely to be erroneously reported.
I included references to BMZ’s failed attempts to commercialize their patented techniques with Derwent.

Following the strictest protocol, the editor of JoCS duly sent this letter to reviewers . After roughly seven months, …

The reviewers’ comments were more than fair. If my arguments were unclear, I was more than happy to reword them and provide additional evidence to get my point across. So I edited my letter to the editor, and re-sent it. …

…within two months or so (the equivalent of overnight in journal-time), the editor sent me a rejection notice with … review, quoted below. This review—this review is sensational. As one afflicted with Hamlet Syndrome, I admire Reviewer #4’s conviction. As someone too often in search of the right phrase to dismiss a crap idea, I take delight in Reviewer #4’s acid pen: I have never seen a reviewer so viciously shit-can a paper before. Reviewer #4 tore my letter to pieces, then burned the pieces. Then poured lye on the ashes. Then salted the earth where the lye sizzled. Then burnt down the surrounding forest, etc.

read on

Nature is the best teacher. Working on real problems makes you smart. … it is not by staring at a hammer that we learn about hammers.

Similarly, scientists who do nothing but abstract work in the context of funding applications are missing out. The best scientists work in the laboratory, in the field; they tinker.

By removing ourselves from the world, we risk becoming alienated. We become strangers to the world around us. Instead, we construct this incoherent virtual reality

Daniel Lemire (@lemire)


The most obvious image of a laughable hipster should be a half-time art-school student whose parents are going to provide him/her with a cushy job and/or money so s/he doesn’t really have to work but can just learn some stuff, party/hang out, make some art, and do a little-of-this little-of-that. Maybe have his/her own record label or vanity company or charity or eat instagrammable food or wear cool clothes or whatever, and be beautiful.




Hey, that actually sounds like a nice life I would like to have for myself.


Since art and learning and performing and consuming of those kinds of things are ends in themselves, it’s like this stereotypical character already has what the rest of us would use up our potential leisure time working to be able to afford. In that case the hipster hatred can be just a form of envy.

The [academic] job market is brutal, we all know that. Grad school is a gamble, and most people shouldn’t take it — a good thing to point out. But this business of “The Big Lie” … deliberately brainwashing students about the “life of the mind” in order to suck them into exploitative … careers…. simply isn’t true.

The lack of data, the bad career advice, the unrealistic expectations, all of these are not the product of some conspiracy, but of a poorly organized and often anarchic system that has developed without any overall plan at all. To improve it, we need to think about the systemic patterns that produce it, not seek out some tenured villains to blame it on.

Take the lack of placement statistics. [It’s not] that “Most departments will never willingly provide that information because it is radically against their interest to do so.” … They won’t provide that data because in most cases they don’t have it. Large graduate programs in the liberal arts are strikingly anarchic places…. It can be difficult to get a list of the graduate students who are enrolled, let alone those who aren’t around any more.



[I]n the late 1920’s and early 1930’s…. There were lots of deep thoughts [in economics], but a lack of quantitative results. … It is usually not of very great practical or even scientific interest to know whether the [causal] influence [of some factor] is positive or negative, if one does not know anything about the strength.

But much worse is the situation when an [outcome] is determined by many different factors at the same time, some factors working in one direction, others in the opposite directions. One could write long papers about so-called tendencies explaining how this … might work…. But what is the … total net effect of all the factors? This question cannot be answered without measures of … strength….

Trygve Haavelmo

Bank of Sweden pseudo-Dynamite Prize Laureate 1989, for work in econometrics


The place where [Satan] was, in my mind, the most successful and first — first successful was in academia. He understood pride of smart people. He attacked them at their weakest.

They were in fact smarter than everybody else and could come up with something new and different — pursue new truths, deny the existence of truth, play with it because they’re smart. And so academia a long time ago fell.
Rick Santorum


Upon my return [to academia, after years of private statistical consulting], I started reading the Annals of Statistics … and was bemused. Every article started with:

Assume that the data are generated by the following model…

followed by mathematics exploring inference, hypothesis testing, and asymptotics…. I [have a] very low … opinion … of the theory published in the Annals of Statistics. [S]tatistics [is] a science that deals with data.

The linear regression model led to many erroneous conclusions that appeared in journal articles waving the 5% significance level without knowing whether the model fit the data. Nowadays, I think most statisticians will agree that this is a suspect way to arrive at conclusions.

In the mid-1980s … A new research community … sprang up. Their goal was predictive accuracy….. They began working on complex prediction problems where it was obvious that data models were not applicable: speech recognition, image recognition, nonlinear time series prediction, handwriting recognition, prediction in financial markets.

The advances in methodology and increases in predictive accuracy since the mid-1980s that have occurred in the research of machine learning has been phenomenal…. What has been learned? The three lessons that seem most important:

  • Rashomon: the multiplicity of good models;
  •           • Occam: the conflict between simplicity and accuracy;
  •           • Bellman: dimensionality — blessing or curse

Leo Breiman, The Two Cultures of Statistics (2001)

(which are: machine learning / artificial intelligence / algorithmists —vs— model builders / statistics / econometrics / psychometrics)