Matthew Effect
A concept introduced by sociologist Robert Merton in 1968 to describe the phenomenon whereby the strong grow stronger and the weak grow weaker. In the AI academic conference ecosystem, this manifests as top-tier conferences monopolizing high-quality resources.
Timeline (last 90 days)
The standardization of data licensing may exacerbate the Matthew effect in data acquisition: large companies with budgets can buy the best proprietary data, while small teams are shut out.
The Matthew effect originates from sociology and describes the phenomenon of cumulative advantage where the strong grow stronger and the weak grow weaker.
The study demonstrated that the Matthew effect mechanism also holds in AI populations: those that gain attention first are more likely to continue gaining attention.
The "Matthew Effect" was proposed by sociologist Robert Merton to describe the phenomenon of cumulative advantage in which the strong grow stronger and the weak grow weaker.
If the algorithm of a paper Q&A system is overly biased toward popular topics, it may reinforce the Matthew Effect, causing obscure but important work to be buried.
马太效应由社会学家Robert K. Merton在1968年正式提出，源自《圣经·马太福音》
All Facts (7)
马太效应（Matthew Effect）这一术语由社会学家罗伯特·默顿于1968年提出
95%UnverifiedThe Matthew effect originates from sociology and describes the phenomenon of cumulative advantage where the strong grow stronger and the weak grow weaker.
50%UnverifiedThe study demonstrated that the Matthew effect mechanism also holds in AI populations: those that gain attention first are more likely to continue gaining attention.
50%UnverifiedIf the algorithm of a paper Q&A system is overly biased toward popular topics, it may reinforce the Matthew Effect, causing obscure but important work to be buried.
50%UnverifiedThe "Matthew Effect" was proposed by sociologist Robert Merton to describe the phenomenon of cumulative advantage in which the strong grow stronger and the weak grow weaker.
50%Unverified马太效应由社会学家Robert K. Merton在1968年正式提出，源自《圣经·马太福音》
50%UnverifiedThe standardization of data licensing may exacerbate the Matthew effect in data acquisition: large companies with budgets can buy the best proprietary data, while small teams are shut out.
50%