The Representation Is Not the Scholarship
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.
Ask the Chefs: Peer Review Capacity — Uncomfortable Truths and Overlooked Conversations
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?
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.
Guest Post — Self-Citation Is How Fields Grow
Today’s post provides a brief analysis of self-referencing and scholarly impact through the lens of stigmergy theory
Guest Post — Preserving the Record, Shaping the Future: Why Diversity Matters in Digital Preservation
Today’s guest post argues that digital preservation is not just a technical problem, but a function that often ignores issues of social justice, quietly codifying existing imbalances in scholarly communication.
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.
The Published Voice: Whose Voice Are We Really Reading?
What happens then to “author voice,” which has always been an important consideration to authors and reviewers alike, if AI is helping researchers with their writing?
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.
Before the Guardrails: Why AI Governance in Research Must Start with Purpose
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.
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.
Academic Freedom for the Win; Open Access Mandate in Germany Declared Unconstitutional
A German court ruled against a mandatory article deposit requirement under Germany’s “secondary publication right” (SPR). Whatever the intentions, SPR is mainly going to contribute to the degradation of the record of science.
The Rise of China’s Scholarly Publishing System Part 2: Navigating China’s Publishing Ambition — Strategic Options for International Publishers
China’s publishing ambitions create genuine competitive pressures, but they also open opportunities for collaboration and highlight challenges that neither side can address alone
The Rise of China’s Scholarly Publishing System Part 1: China’s Journal Ecosystem Is Accelerating
China is no longer simply a major contributor to global research output; it is increasingly becoming a key force shaping the future of scholarly publishing. Understanding what is actually happening, and why, is the necessary first step before considering how publishers should respond.
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.