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E-BART: Jointly Predicting and Explaining Truthfulness., , , and . TTO, page 18-27. Hacks Hackers, (2021)On the role of human and machine metadata in relevance judgment tasks., , , and . Inf. Process. Manag., 60 (2): 103177 (2023)Exploring Data Literacy Levels in the Crowd - the Case of COVID-19., , , and . ICWSM, page 1398-1403. AAAI Press, (2022)JobNoW'19 - 1st International Workshop on Job Knowledge Discovery on the Web & Social Media., , , and . HT, page 305-306. ACM, (2019)Perspectives on Large Language Models for Relevance Judgment., , , , , , , , , and 1 other author(s). ICTIR, page 39-50. ACM, (2023)Let's Agree to Disagree: Fixing Agreement Measures for Crowdsourcing., , , , and . HCOMP, page 11-20. AAAI Press, (2017)Iterative Human-in-the-Loop Discovery of Unknown Unknowns in Image Datasets., , and . HCOMP, page 72-83. AAAI Press, (2021)Preferences on a Budget: Prioritizing Document Pairs when Crowdsourcing Relevance Judgments., , , and . WWW, page 319-327. ACM, (2022)Does Evidence from Peers Help Crowd Workers in Assessing Truthfulness?, , , , and . WWW (Companion Volume), page 302-306. ACM, (2022)Knowledge Graph Quality Management.. SeWeBMeDa@ISWC, volume 2477 of CEUR Workshop Proceedings, page i-ii. CEUR-WS.org, (2019)