"Google has announced it's testing a new AI-powered search tool, Scholar Labs, that's designed to answer detailed research questions. But its demonstration highlighted a bigger question about finding "good" science studies. How much will scientists trust a tool that forgoes typical ways of gauging a study's popularity with the scientific establishment in favor of reading the relationships between words to help surface good research?"
"One metric is the number of times that a study has been cited by other studies since its publication, which loosely translates to a paper's popularity. It's also associated with time: A recently published study might have zero citations or rack up hundreds within a few months; a study from the '90s may tout thousands. Another metric is the "impact factor" of a science journal."
Google is testing Scholar Labs, an AI-powered search tool that identifies main topics and relationships in research queries and is currently available to a limited set of logged-in users. The demo featured brain-computer interfaces (BCIs) and returned a 2024 review in Applied Sciences; Scholar Labs highlighted electroencephalogram signals and leading algorithms as reasons for the match. Scholar Labs does not expose common bibliometric filters such as citation counts or journal impact factors. Citation counts vary with time and can mislead; journal impact factors reflect broadly cited outlets. The absence of these filters may affect researchers' trust in surfaced studies.
Read at The Verge
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