Kirsten the Librarian: The Big List of Google Scholar Alternatives (and the Big Librarian Existential Crisis). “I started writing this in summer of last year, intending it to be a nice little list of tools that would be handy if Google Scholar disappeared or got substantially worse, as a follow up to my LSE Impact blog post.[1] The more I wrote the more I realised that I needed to caveat all my […]

Found in my Calishat Snaps: LinXiv. “Discover, manage, and visualize academic papers from arXiv — run your library on hardware you control, with a modern desktop app, optional AI, Obsidian integration, and an interactive network graph.”

Publishing Under Systematic Pressures

The erosion of research integrity is not primarily a failure of individual character. It is a feature of an academic economy that rewards volume over rigor, metrics over meaning, and speed over depth.

https://businesssciencedaily.com/publishing-under-systematic-pressures/

#businesssciencedaily #papermills #reviewmills #publishing #publicationpressure #researchpapers

Stephen Bainbridge: The Social Science Research Network Has Jumped the Shark. “The research paper series has been an incredibly useful way of staying on top of recent scholarly developments. It gives you a listing of recently posted works and, crucially, each work’s abstract. As such, you know whether or not the paper looks to be of sufficient interest to justify downloading and reading the […]

NBC News: Fake academic journals are publishing AI-generated papers under real professors’ names . “A network of fake academic journals masquerading as legitimate publications has published more than a hundred AI-generated papers in recent months, in some cases using the names of real professors at top universities without their knowledge.”

On the Use of Generative AI

We’ve reached the time of year at Maynooth when academic staff are busy grading projects of various kinds. This year we have to be much mindful of the use of Large Language Models (such as ChatGPT) in written reports as these are much more commonplace now. We anticipated this at the start of the academic year, but now we have to see whether are policies work in practice. In the case of the Computational Physics projects that I have to mark, this also extends to the use of Generative AI in writing code. The approach I take there is that I don’t place an absolute ban, but I require students to declare the use and, crucially, describe what steps they used to test and validate the output. By the time they’ve done that they might as well have written the code themselves!

As well as its effect on teaching, GenAI is having a huge impact on research. In my role as Managing Editor of the Open Journal of Astrophysics I have seen a large increase in submissions of papers in which AI plays some role. These vary from pure “slop” – nonsense papers not worthy of serious consideration – to articles that use AI tools in a perfectly reasonable way to speed up certain aspects of the analysis. I think this is the case for most scientific journals.

The approach we have adopted is similar to the policy on teaching outlined above. It is described by the following section we have added to our “For Authors” page:

Use of Generative AI. We do not operate a blanket ban on the use of Large Language Models (LLMs) or other forms of Generative AI. If you do use such tools, however, you must declare it in the acknowledgments section of your paper. Furthermore, if GenAI methods are used for any form of calculation, analysis, or data visualization you must include an account of what steps you have taken to test and validate these methods. Articles containing direct evidence of the use of GenAI, such as hallucinated references or prompts embedded in the text, will not be accepted.

Since the Open Journal of Astrophysics is an arXiv-overlay journal I should also pass on the information that arXiv is itself developing a policy on the use of LLMs. Although it has yet to appear on the arXiv website, a recent communication on social media states:

If there is incontrovertible evidence of LLM slop in a paper, this means the authors did not take the time to read the LLM output and we can’t trust anything else in the paper. Penalty is 1 year ban from arXiv followed by a requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue.

This will be tantamount to a one-year ban from publishing in OJAp, so urge authors should be be very careful in their use of such methods.

It is likely that these policies will have to be extended as the use of GenAI spreads.