A Synthetic Prediction Market for Estimating Confidence in Published Work

Autor: Rajtmajer, S., Christopher Griffin, Wu, J., Fraleigh, R., Balaji, L., Squicciarini, A., Kwasnica, A., Pennock, D., Mclaughlin, M., Fritton, T., Nakshatri, N., Menon, A., Modukuri, S. A., Nivargi, R., Wei, X., Lee Giles, C.
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scopus-Elsevier
Popis: Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress. We develop a synthetic prediction market to assess the credibility of published claims in the social and behavioral sciences literature. We demonstrate our system and detail our findings using a collection of known replication projects. We suggest that this work lays the foundation for a research agenda that creatively uses AI for peer review.
Databáze: OpenAIRE