Across different creative sectors, existing studies point to measurable and, in some cases, already quantified economic and commercial harm. At the same time, a market for AI licensing is already an established and growing commercial reality and this is continually undermined by unauthorized use.
As scholarly publishing becomes increasingly interconnected, are we paying enough attention to its operational sustainability?
What might be lost if we automate a task without first fully understanding the expertise behind it?
Leadership in academia, publishing, funding, and across the supply chain must coordinate efforts to address research integrity. It is this collective responsibility that will restore and protect the credibility of research.
Today’s guest post is a call to action in support of OpenAlex and its role in author disambiguation.
Today’s guest post takes a deeper view into how researchers are currently engaging with AI and scholarly research, positing that we’re now seeing a shift from agentic discovery to consumption.
Today, we take a dive into the results of SSP’s latest Pulse Check Survey, revealing how our community is grappling with research integrity and its interconnected challenges.
Today’s guest bloggers present a 6-part model for explaining spikes in manuscript submissions and better informing future publishing strategies.
Generative AI has made language tools ubiquitous, but it introduces risks of homogenized texts and semantic slippage.
Closing out Peer Review Week 2026, today’s post suggests that research novelty and capacity for innovation should be given greater priority than clarity and comprehensiveness in peer review.
What if the systems designed to protect scientific rigor are also making it harder for new ideas to emerge? Do different scholarly contributions deserve unique forms of evaluation?
For Peer Review Week 2026, we asked the Chefs two questions: What is one uncomfortable truth about peer review that the scholarly publishing community doesn’t talk about enough? And, beyond recruiting more reviewers, what conversation about peer review capacity are we overlooking?
Identifying AI content is now embedded in Claude models. It doesn’t solve the problem we have. It also creates others.
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.
Today’s post provides a brief analysis of self-referencing and scholarly impact through the lens of stigmergy theory