The Weimar of knowledge
https://giorgio.gilest.ro/the-weimar-of-knowledge/
EXCERPTS: All corners of academia will soon be flooded with AI-generated research. [...] ML conferences have exploded and find it impossible to review what they receive. Maths, CS and physics will fall first, then medicine, biology, chemistry. ... Before discussing solutions, one has to pinpoint the problem.
It is not intellectual ownership: it is crystal clear that the human monopoly on intellect has ended. Screening interviews and other exercises in gatekeeping are as naive as they are futile. [...] The real problem is that any activity based on intellectual capacity is about to be hyper-inflated. Academia will fall first, the rest of the job market will follow.
Academia has of course been inflating for the past 20-30 years, driven by bad career proxies that rewarded the quantity of papers rather than their quality. Perverse incentives, in economic terms: they sustained the publishing industry as we know it and blew up the bubble. AI is now pushing that trend into Weimar territory, and papers will be worth nothing... (
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How scientific publishers are fighting back against fraudulent papermills
https://physicsworld.com/a/how-scie...-fighting-back-against-fraudulent-papermills/
EXCERPTS: While misconduct generally used to be carried out by an individual looking for academic notoriety, it has now evolved into a co-ordinated and commercial activity that is tapping into science’s dependence on metrics when it comes to prestige and career progression. Referred to as “papermills”, these businesses sell authorships, citations, data and even produce full papers. Such papermills have evolved incredibly quickly to evade publisher checks and the peer-review process. Papermills also often market their services to students, promising guaranteed acceptance and citations.
[...] The term “publish or perish” is a well-worn phrase in academia. ... This pressure to publish is partly why publishers and journals have grown so much in recent years. ... But as the popularity of open-access journals has risen, so too has the growth of “predatory” journals.
[...] Researchers are aware that the problem will not resolve itself and that publishers also cannot do all the fraud prevention alone. There are calls for researchers to undergo compulsory training in research ethics, and for journals to require raw instrument file datasets as a criterion for publication, as well as having dedicated conference sessions on combating AI risks.
[...] But all these approaches take time and money... (
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Economics journals are governed by a shrinking elite
https://blogs.lse.ac.uk/impactofsoc...s-journals-are-governed-by-a-shrinking-elite/
EXCERPTS: Describing a new stock-flow framework for analysing journal editorial boards, Alberto Baccini demonstrates how in recent years journal growth in economics has stalled, member turnover has collapsed, and boards have become increasingly stable and insular. This raises critical questions about diversity, innovation, and governance in economics. It also offers a toolkit for studying the same dynamics in other fields.
[...] What explains the structural break in the contemporary period? Is it a function of journal consolidation, the rise of mega-journals, the increasing commercial concentration of publishing, or something else entirely? What are the consequences of closed editorial communities for scientific diversity, innovation, and the career progression of early-career researchers? How do these dynamics relate to citation patterns, author demographics, or the topics that receive attention? These questions are open, and not just for economics.
The study of editorial boards has long been trapped in a static mindset... (
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Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026 (paper is accessible)
https://arxiv.org/abs/2609.19420v1
ABSTRACT: LLMs are rapidly reshaping peer review, making it important to understand how reviewers use them in practice and how different LLM-use policies affect review outcomes. We investigate these questions through a randomized experiment and an anonymous post-survey at ICML 2026, a major machine learning conference involving over 24,000 papers and 17,000 reviewers.
Reviewers were assigned to either a conservative policy prohibiting all LLM use or a permissive policy allowing limited assistance, with randomization among a subset of main-track papers and reviewers. Policy assignment had near-zero effects on final paper decisions, paper scores, and reviewer confidence, although reviews under the permissive policy were 5.5-7% longer.
Post-survey responses (N=1,486) revealed diverse attitudes toward LLMs and substantial noncompliance: 22.5% of conservative-policy reviewers reported using an LLM despite the prohibition, and 36.5% of permissive-policy reviewers reported at least one explicitly disallowed use. We discuss implications for future peer-review policy and tool design. (
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