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.
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.
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.
Today’s guest post warns the community that, in the AI era, we cannot keep building the pieces that lay the foundation and letting someone else own what gets built on top of it.
Research integrity demands stronger safeguards in order to protect the scholarly record. But for editors, when new integrity measures are introduced, they do not replace existing tasks; they are added alongside them, compounding workloads and responsibilities.
Robert Harington reflects on our addiction to speed and advocates for slow scholarly publishing and the inherent beauty of not always being first.
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.
Today’s guest post introduces the YCR-index as an alternative to measuring value with raw citation counts.
Today’s post asks us to acknowledge the role of AI in peer review and ensure practical guidance and policies that help scholars respond with consistency and confidence.
The future of scholarly communication will not be determined by how powerful AI becomes, but by whether the research community remains clear about the purpose those capabilities are meant to serve and whether it can govern them together.
While it’s true that AI may be viewed as “legitimate,” it’s far from universally loved. Understanding that distinction tells us something important about how the technology may ultimately be adopted — and governed — within academia.
Today’s post explores what happens to the scholarly content platform when AI agents become the users.
A conversation on AI retrieval, the provenance problem, and the shared infrastructure scholarly publishing needs.
Today’s post shares the results of an initiative designed to answer the question: what would it actually take to build a publishing model fit for the research ecosystem we have now, rather than the one we inherited?
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.