AI and Scholarly Reading: Foxes versus Hedgehogs
Some readers are foxes, and some are hedgehogs. What kind of reader is AI, and how will that impact both the practice of science and the business of journal publishing?
Some readers are foxes, and some are hedgehogs. What kind of reader is AI, and how will that impact both the practice of science and the business of journal publishing?
The journal portfolio, platform, or membership is no longer enough, as AI transforms how research is discovered and consumed. How will your organization adapt?
Today’s guest bloggers posit four important trends in scholarly communications that our community should be ready to tackle.
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.
In this AI era, establishing trust and validating article versions with persistent identifiers (PIDs) is more important than ever.
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.
Four Bangladeshi academics touch upon different aspects of academic publishing by linking them with the SDGs: accessing research; equity in co-authorship; editorial leadership; and SDG framing.
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.
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.
A conversation on AI retrieval, the provenance problem, and the shared infrastructure scholarly publishing needs.
Federated identity should be a natural fit for library access. So why isn’t it?
AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.
Today’s post shares the results of an initiative designed to answer the question: what would it actually take to build a publishing model fit for the research ecosystem we have now, rather than the one we inherited?
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.
Today, we feature a friendly debate on the question: which parts of the research lifecycle should be more automated, and which require more of a human touch — and why?