Large language models make it entirely trivial to generate endless amounts of seemingly plausible text. There’s no need to be cynical to see the virtual inevitability of unending waves of algorithmically tuned AI-generated uninformation: the market forces are in place and they will be relentless.

Language change has been the focus of my research for over twenty years now, so when I taught second semester linguistics at Saint John’s University, I was very much looking forward to teaching a unit focused on change.  I had been working to replace constructed examples with real data, so I took a tip from my natural language processing colleague Dr. Wei Xu and turned to SparkNotes.

Yesterday I was reminded of what got me interested in economics. I’ll preface this by saying that I make my living as a substitute teacher in Toronto. It’s not glamorous, but it pays the bills. It gives me time to do research from outside academia. When I’m in high school classrooms, I always browse the posters on the wall. It’s funny what you see. You find things (both good and bad) that you’d never see in institutions of ‘higher learning’.

After much postponement, writing the final report for my NWO Veni grant (2015-2018) turned out to be an unexpected pleasure. It made me realise a couple of things — key among them the role of serendipity in shaping fundamental research. The project was called “Towards a science of linguistic depiction”. Looking at the publications that came out of it and at imperfect indicators like citations, it’s made some useful contributions.

For perhaps ten years now, the future of scientific publishing has been hotly debated. The traditional models are often thought to be badly broken, although convergence to a consensus of what a better model should be is not apparently close. But to my mind, much of this debate seems to miss one important point, how to publish data.

One of the key tasks scientists need to master is how to manage bibliographic information: collecting relevant literature, building a digital library, and handling citations and bibliographies during writing. This tutorial introduces Zotero (www.zotero.org), an easy to use reference management tool made by scholars for scholars. The tutorial covers the basics of using Zotero for collecting, organizing, citing and sharing research.

On Saturday I found out that Alan Hudson died. Alan was my doctoral advisor at the University of New Mexico until his retirement in 2005, and a source of support after that. I first met Alan when I visited the UNM Linguistics Department in 1997. Alan welcomed me into his office with a broad smile, and asked, “So Angus, have you made up your mind about whether you want to come here?” “Well…” I said.

The big buzz over the past few years has been Data Science. Corporations are opening Data Science departments and staffing them with PhDs, and universities have started Data Science programs to sell credentials for these jobs. As a linguist I’m particularly interested in this new field, because it includes research practices that I’ve been using for years, like corpus linguistics and natural language processing.

In January I wrote that I believe online learning is possible, but I have doubts about whether online courses are an adequate substitute for in-person college classes, let alone an improvement. One of those doubts concerns trust and intellectual honesty. Any course is an exchange. The students pay money to the college, the instructor gets a cut, and the students get something of value in return. What that something is can be disputed.

Since July 2016 I have been working as Associate Application Systems in the Teaching and Learning Applications group at Columbia University. I have developed several apps, including this Photo Roster, an LTI plugin to the Canvas Learning Management System. The back end of the Photo Roster is written in Python and Flask.