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.
Guest Post – A Year of Trends Beyond Scholarly Comms: What You Need to Know
Today’s guest bloggers posit four important trends in scholarly communications that our community should be ready to tackle.
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 — Your Next Reader Is a Machine, and We Are Ready
Machine readers replacing human readers can sound like a threat. Today’s post argues that our community is well prepared to face these challenges and seize the opportunities.
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.
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.
Making AI Use of Scholarly Content Traceable, Measurable, and Trustworthy: A Meeting Report from Cambridge Scholarly AI Workshop
A Cambridge workshop proposes new standard work to support provenance, attribution and metrics in scholarly communications AI tools.
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.
Guest Post — Fixing the Leaky Metadata Pipeline: A Conversation with the Creator of Research Nexus Score
Today, guest blogger Rob Johnson speaks with the creator of Research Nexus Score, and observes that metadata quality has gone from a niche concern to a sector-wide anxiety.
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.