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dc.contributor.authorMarquardt, Kyle L.
dc.contributor.authorPemstein, Daniel
dc.contributor.authorSanhueza Petrarca, Constanza
dc.contributor.authorSeim, Brigitte
dc.contributor.authorWilson, Steven Lloyd
dc.contributor.authorBernhard, Michael
dc.contributor.authorCoppedge, Michael
dc.contributor.authorLindberg, Staffan I.
dc.date.accessioned2017-10-02T09:37:15Z
dc.date.available2017-10-02T09:37:15Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/2077/53886
dc.description.abstractRecent work suggests that crowd workers can replace experts and trained coders in common coding tasks. However, while many political science applications require coders to both and relevant information and provide judgment, current studies focus on a limited domain in which experts provide text for crowd workers to code. To address potential over-generalization, we introduce a typology of data producing actors - experts, coders, and crowds - and hypothesize factors which affect crowd-expert substitutability. We use this typology to guide a comparison of data from crowdsourced and expert surveys. Our results provide sharp scope conditions for the substitutability of crowd workers: when coding tasks require contextual and conceptual knowledge, crowds produce substantively dierent data from coders and experts. We also find that crowd workers can cost more than experts in the context of cross-national panels, and that one purported advantage of crowdsourcing - replicability - is undercut by an insucient number of crowd workers.sv
dc.language.isoengsv
dc.relation.ispartofseriesWorking Paperssv
dc.relation.ispartofseries2017:53sv
dc.titleExperts, Coders, and Crowds: An analysis of substitutabilitysv
dc.typeTextsv
dc.contributor.organizationV-Dem Institutesv


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