Unverified50% confidenceFactExact time
The core insight of the research is that a simple information rule—whether or not one can see others' choices—is sufficient to systematically alter the quantity, breadth, and distributional structure of scientific attention in AI populations.
1
Sources
50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
Sources
Related Claims
UnverifiedThe study finds that AI reviewers have a systematic calibration discrepancy with human experts in scoring scale.69% similarUnverified对初级分析师而言，将脱敏的真实项目拿到数据社区请教，比抽象提问能获得更有深度的反馈68% similarUnverifiedHaving small groups deliberate first and then averaging the consensus estimates of each group produces accuracy that surpasses classical wisdom of the crowd.68% similarUnverifiedThere are two systems for evaluating the value of AI: the public scale, which uses convenience and immediacy as standards, and the scientific research scale, which uses the advancement of the boundaries of knowledge as its standard.67% similarUnverifiedSurvey data from Stanford University's HAI Institute shows that users' regard for data security and privacy protection when choosing AI assistants has surpassed feature richness, ranking among the top three decision factors.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/942723API
curl https://kongchang.com/api/v1/knowledge/claims/942723MCP
get_claim(id=942723)