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Read-Agree-Predict: A Crowdsourced Approach to Discovering Relevant Primary Sources for Historians

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dc.contributor.authorWang, Nai-Chingen
dc.contributor.authorHicks, Daviden
dc.contributor.authorQuigley, Paulen
dc.contributor.authorLuther, Kurten
dc.contributor.departmentComputer Scienceen
dc.contributor.departmentHistoryen
dc.contributor.departmentSchool of Educationen
dc.date.accessioned2021-09-29T18:53:44Zen
dc.date.available2021-09-29T18:53:44Zen
dc.date.issued2019en
dc.description.abstractHistorians spend significant time looking for relevant, high-quality primary sources in digitized archives and through web searches. One reason this task is time-consuming is that historians’ research interests are often highly abstract and specialized. These topics are unlikely to be manually indexed and are difficult to identify with automated text analysis techniques. In this article, we investigate the potential of a new crowdsourcing model in which the historian delegates to a novice crowd the task of labeling the relevance of primary sources with respect to her unique research interests. The model employs a novel crowd workflow, Read-Agree-Predict (RAP), that allows novice crowd workers to label relevance as well as expert historians. As a useful byproduct, RAP also reveals and prioritizes crowd confusions as targeted learning opportunities. We demonstrate the value of our model with two experiments with paid crowd workers (n=170), with the future goal of extending our work to classroom students and public history interventions. We also discuss broader implications for historical research and education.en
dc.description.sponsorshipThis research was supported by U.S. National Historical Publications and Records Commission Grant DH50013-15.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.15346/hc.v6i1.8en
dc.identifier.issn2330-8001en
dc.identifier.issue1en
dc.identifier.urihttp://hdl.handle.net/10919/105111en
dc.identifier.volume6en
dc.language.isoenen
dc.publisherHuman Computation Instituteen
dc.rightsCreative Commons Attribution 3.0en
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.titleRead-Agree-Predict: A Crowdsourced Approach to Discovering Relevant Primary Sources for Historiansen
dc.title.serialHuman Computationen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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