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.
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
In this AI era, establishing trust and validating article versions with persistent identifiers (PIDs) is more important than ever.
How has the social media landscape in scholarly communications changed, according to data from the recent Pulse Check, past community surveys, and checked real, live marketers?
Today’s guest post introduces the YCR-index as an alternative to measuring value with raw citation counts.
A Cambridge workshop proposes new standard work to support provenance, attribution and metrics in scholarly communications AI tools.
Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.
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.
China’s publishing ambitions create genuine competitive pressures, but they also open opportunities for collaboration and highlight challenges that neither side can address alone
A powerful way to quantify article quality has been hiding in plain sight. It’s time to bring data citations into the limelight.
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.
The threat of zero-click search makes organizational brand more important than ever and presents a huge opportunity.
ScholarOne saw a submission surge in the first quarter of 2026 — evidence that AI is increasing the strain on peer review’s social contract with researchers.
Today’s guest post proposes a method for identifying, measuring, and managing robotic usage of scholarly content.
A look at the data from the second year of the SSP Compensation and Benefits Benchmarking Study.