In the first of two disapointing scholarly-communication announcements last week, Jisc announced its report on progress towards open access in the UK. The key finding is: But that’s not the part that disappoints me. Here’s the part that disappoints me: Sometimes I think people don’t know what “transitional” means.
Prompted by a post on Mastodon (which, like all Mastodon posts, I can no longer find), I asked ChatGPT to tell me about my own papers. The response started out well but quickly got much worse. I will indent my comments on its response. Q. What are some articles written by Michael P. Taylor? A. Michael P. Taylor is a vertebrate paleontologist and open science advocate.
I was a bit shaken to read this short article, Submit It Again! Learning From Rejected Manuscripts (Campbell et al. 2022), recently posted on Mastodon by open-access legend Peter Suber. For example: Let’s pick this apart a bit. “Because they recently published a similar article” ? What is this nonsense.
I’m sure you’ve seen things like ChatGPT in the news: programs that can carry out pretty convincing conversations. They are known as Large Language Models (LLMs) and are frequently referred to as being Artificial Intelligence (AI) — but I really don’t like that designation as it implies some understanding.
“And in conclusion, this new fossil/analysis shows that Lineageomorpha was more [here fill in the blank]: diverse morphologically varied widely distributed geographically widely distributed stratigraphically …than previously appreciated.“ Yes, congratulations, you’ve correctly identified that time moves forward linearly and that information accumulates.
I think we’ve all had enough of the Impact Factor as a way of measuring the quality of journals.
In the last post, I catalogued some of the reasons why Scientific Reports , in its cargo-cult attempts to ape print journals such as its stablemate Nature , is an objectively bad journal that removes value from the papers submitted to it: the unnatural shortening that relagates important material into supplementary information, the downplaying of methods, the tiny figures that ram unrelated illustrations into compound images, the
As I was figuring out what I thought about the new paper on sauropod posture (Vidal et al. 2020) I found the paper uncommonly difficult to parse.
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It’s common to come across abstracts like this one, from an interesting paper on how a paper’s revision history influences how often it gets cited (Rigby, Cox and Julian 2018): This tells us that a larger number of revisions leads to (or at least is correlated with) an increased citation-count. Interesting! Immediately, I have two questions, and I bet you do, too: 1. What is the size of the effect?