Abstract
A system and method are disclosed to automatically estimate the quality of individual papers published by a scholar in a web search engine. The index of search engine consists of a graph model which has three main elements - papers, scholars and publications. Paper edges such as cites-presumes true, replicates, fails-to-replicate and meta-analyzes are the features where most of the power resides. This graph-dependent method provides truth-seeking incentives for scholars to obtain higher or lower rating for their published papers based on the information estimated from the paper edges. The credibility metrics are preserved and updated live, as they propagate through the network. Scores are given to individual scholars, papers, and publications based on their credibility metrics. Journalists and laypeople may have an easy, minimum-expertise way of assessing the importance of a particular scholar or a publication.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Cardozo, Alexandros Salazar, "A Scholarly Paper Index With Strong Truth-Seeking Incentives", Technical Disclosure Commons, (January 31, 2017)
https://www.tdcommons.org/dpubs_series/385