We Can Now Track AI-generated Content. It Doesn’t Solve the Real Problem.
Identifying AI content is now embedded in Claude models. It doesn’t solve the problem we have. It also creates others.
AI Use in The Scholarly Kitchen
Some thoughts on the reader response the Scholarly Kitchen has seen regarding AI use by authors, a recent Nature News article on the efficacy of AI detection tools, and why it’s important to keep experimenting to figure out what’s ethically acceptable.
Guest Post — AI Can Find the Article. Can It Tell Which Version It Used?
In this AI era, establishing trust and validating article versions with persistent identifiers (PIDs) is more important than ever.
Reading Between the Lines, Part 2: Cognitive Debt and the Adaptive Publisher
The central risk of AI in scholarly publishing is therefore not hallucination but cognitive debt: the gradual erosion of the human capacities that give scholarship its originality, integrity, and meaning.
Reading Between the Lines, Part 1: A Cognitive Framework for AI in Scholarly Publishing
AI is now a permanent feature of the scholarly landscape. The question is no longer whether it should be used, but how it should be understood and governed.
What Authors Expect Now, and Why Publishing Hasn’t Kept Up: An Interview with Jennifer Fleet of Aries Systems and Christian Grubak of ChronosHub
Today Alice Meadows interviews Jennifer Fleet (Aries Systems) and Christian Gruback (ChronosHub) about changing author needs and how their recently announced partnership is an effort to address them.
Open Science 2.0: Building Understanding in an AI-Mediated World
Open Science 2.0 must focus not only on access, but also on trust, interpretation, learning, and effective communication. The challenge facing the scholarly publishing ecosystem is ensuring that what is open is also trustworthy, understandable, and genuinely useful.
Guest Post — Beyond the Prestige: Why Scientific Impact is More Than a Numbers Game
Today’s guest post introduces the YCR-index as an alternative to measuring value with raw citation counts.
Attribution, Provenance, Reference, Citation, and AI for Research Applications – Understanding the Differences
Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.
Building Scholar-Ready AI: A Conversation with Todd Toler
A conversation on AI retrieval, the provenance problem, and the shared infrastructure scholarly publishing needs.
Guest Post — Advancing Federated Identity in the Library Ecosystem
Federated identity should be a natural fit for library access. So why isn’t it?
The User Has Changed. Has Scholarly Publishing?
For scholarly publishers, the user has changed faster than the systems designed to serve them, and the gap between the two is where most of the difficult work is happening.
Seven Questions about CC Signals
With CC Signals, Creative Commons wants to help authors put rules on use of their licensed content for AI training. The problem is, one of the licenses already permits free and unlimited reuse of that content, for any and all purposes. And the licenses are irrevocable.
Guest Post — A Friendly Debate: Where Automation Will (And Should) Land in the Research Lifecycle
Today, we feature a friendly debate on the question: which parts of the research lifecycle should be more automated, and which require more of a human touch — and why?
Filter, Not Funnel: What Zero-click Means for Brand and Engagement
The threat of zero-click search makes organizational brand more important than ever and presents a huge opportunity.