The big scandal this weekend: Peter Boghossian and James Lindsay pulled a hoax on a social-science journal by getting a deliberately nonsensical paper published there, and then crowed that this demonstrates the field of gender studies to be “crippled academically.” However, when people with a measure of sense examined B&L’s stunt, they found it to be instead evidence that you can get any crap published if you lower your standards far enough, particularly if you’re willing to pay for the privilege and you find a journal whose raison d’être is to rip people off. Indeed, B&L’s paper (“The conceptual penis as a social construct”) was rejected from the first journal they sent it to, and it got bounced down the line to a new and essentially obscure venue of dubious ethical standing. Specifically, I can’t find anybody who had even heard of Cogent Social Sciences apart from spam emails inviting them to publish there. This kind of bottom-feeding practice has proliferated in the years since Open Access publishing became a thing, to unclear effect. It hasn’t seemed in practice to tarnish the reputation of serious Open Access journals (the PLOS family, Scientific Reports, Physical Review X, Discrete Analysis, etc.). Arguably, once the infrastructure of the Web existed, some variety of pay-to-publish scam was inevitable, since there will always be academics angling for the appearance of success—as long as there are tenure committees.

Boghossian and Lindsay made the triumphant announcement of their hoax in Skeptic, a magazine edited by Michael Shermer. And if you think that I’ll use this as an occasion to voice my grievances at Capital-S Skepticism being a garbage fire of a movement, you’re absolutely correct. I agree with the thesis of Ketan Joshi here:

The article in Skeptic Magazine highlights how regularly people will vastly lower their standards of skepticism and rationality if a piece of information is seen as confirmation of a pre-existing belief – in this instance, the belief that gender studies is fatally compromised by seething man-hate. The standard machinery of rationality would have triggered a moment of doubt – ‘perhaps we’ve not put in enough work to separate the signal from the noise’, or ‘perhaps we need to tease apart the factors more carefully’.

That slow, deliberative mechanism of self-assessment is non-existent in the authorship and sharing of this piece. It seems quite likely that this is due largely to a pre-existing hostility towards gender studies, ‘identity politics’ and the general focus of contemporary progressive America.

Boghossian and Lindsay see themselves as the second coming of Alan Sokal, who successfully fooled Social Text into publishing a parody of postmodern theory-babble back in 1999. But after the fact, Sokal said the publication of his hoax itself didn’t prove much at all, just that a few people happened to be asleep at the wheel. (His words: “From the mere fact of publication of my parody I think that not much can be deduced.”) Then he wrote two books of footnotes and caveats to show that he had lampooned some views he himself held in more moderate form.

Meanwhile, Steven Pinker—who happily boosted the B&L hoax to his 310,000 Twitter followers—strips all the technical content out of physics, mixes the jargon up with trite and folksy “wisdom,” and uses the result to support pompous bloviation.

… Which, funny story, is one of the main things that Alan Sokal was criticizing.

I gotta quote this part of B&L’s boast:
Continue reading Bogho-A-Lago

Simple Equations are No Good When the Variables are Meaningless

A few weeks back, I reflected on why mathematical biology can be so hard to learn—much harder, indeed, than the mathematics itself would warrant.

The application of mathematics to biological evolution is rooted, historically, in statistics rather than in dynamics. Consequently, a lot of model-building starts with tools that belong, essentially, to descriptive statistics (e.g., linear regression). This is fine, but then people turn around and discuss those models in language that implies they have constructed a dynamical system. This makes life quite difficult for the student trying to learn the subject by reading papers! The problem is not the algebra, but the assumptions; not the derivations, but the discourse.

Recently, a colleague of mine, Ben Allen, coauthored a paper that clears up one of the more confusing points.

Hamilton’s rule asserts that a trait is favored by natural selection if the benefit to others, $B$, multiplied by relatedness, $R$, exceeds the cost to self, $C$. Specifically, Hamilton’s rule states that the change in average trait value in a population is proportional to $BR – C$. This rule is commonly believed to be a natural law making important predictions in biology, and its influence has spread from evolutionary biology to other fields including the social sciences. Whereas many feel that Hamilton’s rule provides valuable intuition, there is disagreement even among experts as to how the quantities $B$, $R$, and $C$ should be defined for a given system. Here, we investigate a widely endorsed formulation of Hamilton’s rule, which is said to be as general as natural selection itself. We show that, in this formulation, Hamilton’s rule does not make predictions and cannot be tested empirically. It turns out that the parameters $B$ and $C$ depend on the change in average trait value and therefore cannot predict that change. In this formulation, which has been called “exact and general” by its proponents, Hamilton’s rule can “predict” only the data that have already been given.