Here’s a striking result that caught me off guard the other day. It came up in a facebook thread, and judging by the discussion there it caught a few other people in this neighbourhood off guard too. The short version: chances are “self-expecting” pretty much if and only if they’re “self-certain”. Less cryptically: the chance of a proposition equals its expected chance just in case the chance function assigns probability 1 to itself being the true chance function, modulo an exception to be discussed below.... Read more

How much does a PhD from a prestigious program help you on the job market in academic philosophy? It makes a big difference to where you get a tenure-track job, if you do get one (see here). It also seems to make some difference to whether you get a tenure-track job (though maybe not as much as one might have thought: see here). But here I want to consider whether it makes a difference to how long it takes to get a tenure-track job, if you do get one.... Read more

This post is the second of two devoted to an idea of David Wallace’s: applying Google’s PageRank algorithm to the APDA placement data. Part 1 Part 2 Source on GitHub Last time we looked at the motivation and theory behind the idea. Now we’ll try predicting PageRanks. Can students who care about PageRank use the latest PGR to guesstimate a program’s PageRank 5 or 10 years in the future?... Read more

This is the first of two posts devoted to an idea of David Wallace’s. Part 1 Part 2 Source on GitHub Suppose you pick a philosophy PhD program at random and you go visit their website. There you pick a random person from the faculty list and see where they got their PhD. Then you go to that program’s website and repeat the exercise: pick a random faculty member, see where they did their PhD, and go to that program’s website.... Read more

In the previous post we saw there’s about a \$35\$% chance a given referee will agree to review a paper for Ergo. And on average it takes about \$5.8\$ tries to find two referees for a submission. The full empirical distribution looks like this: But there’s also an a priori way of exploring an editor’s predicament here, by using a classic model: the negative binomial distribution. So I thougth I’d make a little exercise of seeing how well the model captures the empirical reality here.... Read more

Finding willing referees is one of the more tedious parts of an editor’s job. And with all the talk about how overloaded the peer-review system is, it’s worth pausing to examine just how hard it is to find referees. Well, at Ergo it takes on average 5.8 tries before we find two referees to review a submission. The following plot gives the full picture. So most submissions take six or fewer invites, and the overwhelming majority require fewer than 10.... Read more

If waiting to hear back from journals makes you as twitchy as it makes me, you might appreciate Waiting for the Editor. It’s a little app that displays wait time forecasts and data from the APA Journal Survey. It has two kinds of display, based on my earlier post about the APA survey. You can view scatterplots: Or ridgeplots: Note that the ridgeplots can be misleading. They treat old data and new the same.... Read more

In 2009 Andrew Cullison set up an ongoing survey for philosophers to report their experiences submitting papers to various journals. For me, a junior philosopher working toward tenure at the time, it was a great resource. It was the best guide I knew to my chances of getting a paper accepted at Journal X, or at least getting rejected quickly by Journal Y. But I always wondered about self-selection bias.... Read more

A Times Higher Education piece making the rounds last week found that most published philosophy papers are never cited. More exactly, of the studied philosophy papers published in 2012, more than half had no citations indexed in Web of Science five years later. At Daily Nous, the discussion of that finding turned up some interesting follow-up questions and findings. In particular, Brian Weatherson found quite different figures for papers published in prestigious philosophy journals.... Read more

In this post we’ll improve our training algorithm from the previous post. When we’re done we’ll be able to achieve 98% precision on the MNIST data set, after just 9 epochs of training—which only takes about 30 seconds to run on my laptop. For comparison, last time we only achieved 92% precision after 2,000 epochs of training, which took over an hour! The main driver in this improvement is just switching from batch gradient descent to mini-batch gradient descent.... Read more