As AI research assistants learn our preferences, they may also become better at mirroring our assumptions. Avoiding that outcome will require more than a better prompt.
The opportunity presented by AI is not to remove the human layer of scholarship. It is to recognize why that layer has always mattered.
Identifying AI content is now embedded in Claude models. It doesn’t solve the problem we have. It also creates others.
Today’s post provides a brief analysis of self-referencing and scholarly impact through the lens of stigmergy theory
From 19th-century beef tea to paper mills and generative AI, the challenge is no longer detection alone; it is preventing bad claims from becoming embedded knowledge.
Today’s guest post asks: Do trust markers and other systematic fraud-prevention practices exclude unaffiliated authors from contributing to the scholarly record?
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.
An interview with Natalie Ridgway about new guidelines from the Committee on Publication Ethics.
Whether peer review needs revolution or evolution may be the wrong question. The more pressing one is whether the academic community is prepared to let scientific ideas be published, contested, and refined in public, rather than behind closed doors.
Today, we asked the community: What key message(s) do you want policymakers to take from the responses submitted to the OMB?
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.
Today’s post is the second in a series arguing that fractures appearing across research systems are symptoms of a mismatch within today’s research ecosystem.
Today’s guest blogger questions the operating model around scholarly research and asks why these capabilities are so difficult to sustain at scale.
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.