TL;DR: We can detect individuals with a high probability of being involved in milling papers. The question is: how should we respond? A few years ago, my bike was stolen. It was an organised job. The thieves arrived after dark, cut a hefty lock clean off, and my bike disappeared silently forever. I imagined it dismantled and sold off into some vast black market network. Poor bike. The police admitted that they weren’t going to do anything.
TL;DR: the rate of Papermill Alarm alerts is significantly higher in gold OA content than it is in subscription content. If you’re concerned about papermills, Clear Skies offers the Papermill Alarm: the world’s leading papermill detection tool. Get in touch for a demo. I’m a big believer in Open Science.
So, we’ve established that papermills like to use templates. We see templates in referee reports and in the text of cookie-cutter research papers. There’s an important insight here: Templates are used in legit academic behaviour as well as in industrial research fraud. So we need to be careful — we can’t assume that templates are necessarily a sign of research fraud.
There’s this phrase used by some technologists: ‘epistemic security’. Epistemic security has to do with things like the spreading of misinformation on social media. I.e. if we spend enough time on social media reading unreliable information, how does that influence us? That’s a matter of epistemic security. Epistemic security is interesting because if you start a conversation about epistemic security at a party, they don’t invite you back.
TL;DR: peer-review times appear to have been growing for a long time. The effects of COVID-lockdowns on peer-review are surprising. A few months ago, I was invited to referee a research paper. So, I guess the editor thought that I was one of the 2 best people in the entire world to review this thing. That’s how it works, right? Flattered, I flagged the editor’s email to signal its importance and I got straight to work!
The Papermill Alarm draws a line between papers which have the characteristics of papermill-products and those that don’t. Given data like that shown below, where red dots represent papermill-products and green dots represent normal papers, where should we draw that line?
TL;DR: There’s been a recent major upgrade to the private Papermill Alarm. It looks like we can predict the inclusion of journals on the Chinese Academy of Sciences (CAS) lists. You know how data scientists make predictions?
This is Ralph. How tall is Ralph? It seems simple, you could just hold a ruler up to the screen. But when you look at the ruler and use it to measure Ralph, are you actually measuring Ralph , or are you measuring the ruler and using that as a proxy ? How accurate is your measurement? Are you including fur in the measurement? What if Ralph were to stand on his hind legs, like a mighty bear — how tall would he be then?
There’s a quote attributed to Ernest Rutherford: “That which is not measurable is not science. That which is not physics is stamp collecting”. I think his point was that a lot of scientific work is just documenting things. In Rutherford’s day, there was a lot of exciting new creative work happening in physics, so perhaps physics seemed special to him.
Recently, I gave a presentation on the APIs for Papermill Detection offered by Clear Skies Ltd. I also touch on a newer service called the Clear Skies Standard Report. More on that in future posts… :) Here’s the video: https://medium.com/media/ee5a04aaed9fc53d2748e64516178ebe/href Would you like to know more?
