The construction of gothic cathedrals like Chartres was governed not by blueprints but by “talk, tradition, and templates” — at least that is what Turnbull has compellingly argued. When you come across such a neatly alliterative triad, there are two ways you can go. You can adopt the terms in an unexamined way and rely on their alliterative power.

A how-to guide for academics who want to have their name on lots of papers.

DALL-E, a new image generation system by OpenAI, does impressive visualizations of biased datasets. I like how the first example that OpenAI used to present DALL-E to the world is a meme-like koala dunking a baseball leading into an array of old white men — representing at one blow the past and future of representation and generation.

There is a considerable halo-effect attached to JIFs, whereby an article that ends up in a high IF journal (whether by sheer brilliance or simply knowing the right editor, or both) is treated, unread, with a level of veneration normally reserved for Wunderkinder. Usually this is done by people totally oblivious to network effects, gatekeeping and institutional biases.

Lezenswaardig: een groep jonge medici ageert tegen de marketing-wedstrijd waarin volgens hen narratieve CVs in kunnen ontaarden — de nieuwste bijdrage aan het Erkennen & Waarderen-debat. Maar niets is wat het lijkt. Over evidence-based CVs, kwaliteit & kwantificatie. Eerst dit: de brief benoemt het risico dat je met narratieve CVs een soort competitie krijgt tussen verhalen.

I’m starting a postdoc in July.

I have other things to do but one day I’ll enlarge on the insidious effects of elevating this cursed little histogram of “Research output per year” as the single most important bit of information about academics at thousands of universities that use Elsevier Pure. Consider this mini-rant my notes for that occasion. Most importantly, we DO NOT write per year. Our careers are too diverse and precarious to measure output that way.

With Times Higher Education writing about citation gaming and hyperprolific authors (surely not unrelated) I hope we can save some of our attention for what Uta Frith and others have called slow science.

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.