The AI Transparency Trap
The absence of AI doesn’t guarantee trustworthy research. And neither does disclosing AI use.
The absence of AI doesn’t guarantee trustworthy research. And neither does disclosing AI use.
Today’s guest blogger argues that if we want scholarly publishing to be worth anything in a generation, we must acknowledge the risks we run by relying on intelligent automation.
Libraries and their vendor partners can build registries and metadata feeds that make MCP connectors for scholarly content findable, accessible, and usable.
Today’s guest post is a call to action in support of OpenAlex and its role in author disambiguation.
Amidst the onslaught of industry and global changes, opportunities, and advancements, which signals deserve action now, which are context, and which are weather?
Thoughts on the Frankfurt Book Fair 2026: how do we turn trusted content into useful knowledge — and help people act on it?
Today’s guest post reflects on how implementation, multilingual discovery, and cross-industry collaboration turned a metadata file into a map of the scholarly information supply chain.
Today’s guest blogger reflects on why the next phase of AI adoption needs more evaluation, collaboration, and human judgment.
The community comes together to launch TRACE, a project to improve generative AI systems’ interoperability, verifiability and usage tracking.
The problem with AI may not be that machines are growing more intelligent. It may be that we are becoming increasingly skilled at amplifying a highly capable, but limited form of cognition.
Academic and research libraries, as well as public libraries, historical societies, and other organizations, hold extensive collections of rare and unique materials, such as archival and manuscript collections. These materials form an essential part of humanity’s cultural and historical record, and they are the vital source material for many humanistic fields. I believe that a second digital transformation is at hand for distinctive collections. How can library leaders establish a strategy for their distinctive collections that is fit for purpose today?
Some readers are foxes, and some are hedgehogs. What kind of reader is AI, and how will that impact both the practice of science and the business of journal publishing?
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?
Today’s guest post challenges our community to face tendencies toward groupthink and address the incentive structures that resist adaptation and innovation.
The journal portfolio, platform, or membership is no longer enough, as AI transforms how research is discovered and consumed. How will your organization adapt?