Preprint moderators have to strike 'a really, really difficult balance' between rapidly sharing new research and protecting the community from flawed or harmful material, says Natascha Chtena, who studies scholarly communication and open science at Simon Fraser University in Vancouver, Canada.
The study found that informal ties are a more relevant predictor of publication success than formal collaborations, even after controlling for various factors such as gender and institutional prestige.
Modern scientific societies are increasingly vulnerable due to their dependence on membership fees and journal subscriptions, which are being challenged by the rise of virtual networking and open-access publishing.
I'm less interested in topics than in questions, and I'm less interested in publishing than I am in curation. When I've testified before Congress or dealt with an appropriations bill or a budget negotiation, this question, of what is the return on investments when you're doing R&D, comes up quite often. It's been asked by economists in very formal ways since at least the 1950s, but the data and the methods that were available were really not very strong.
Librarians have been actively collaborating and talking about it almost every day, whether it's creating tutorials and digital learning objectives or thinking about the conversations to have with instructors. It can feel like cognitive dissonance to be actively working with AI on a regular basis and also saying we're constantly thinking about the harms and the biases.
A few years ago, I put together what I felt was a truly innovative concept, which I presented in a conference poster at an international meeting in my field. After the presentation, I spoke to another early-career scientist about my work and how it might apply to their findings. Two years later, they scooped me by publishing a preprint paper that presented my idea, with many of the same verbal formulations and an identical flow of ideas, without any acknowledgement or attribution to my work.
Bias risks: AI can amplify inequalities, like mislabeling non-native English writing as AI-generated. Privacy concerns: Schools face rising cyberattacks, and data misuse risks are high. Accountability: Human oversight is crucial to prevent over-reliance on AI.
