@prefix this: <http://purl.org/np/RA6DGxa22s4r8PSDwBb5WA1wypoTUsEVYEW_QwsWFZb68> .
@prefix sub: <http://purl.org/np/RA6DGxa22s4r8PSDwBb5WA1wypoTUsEVYEW_QwsWFZb68#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix pav: <http://purl.org/pav/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix doco: <http://purl.org/spar/doco/> .
@prefix c4o: <http://purl.org/spar/c4o/> .
sub:Head {
  this: np:hasAssertion sub:assertion ;
    np:hasProvenance sub:provenance ;
    np:hasPublicationInfo sub:pubinfo ;
    a np:Nanopublication .
}
sub:assertion {
  sub:paragraph c4o:hasContent "To be more efficient than traditional outsourcing (or even in-house resources), microtasks need to be highly parallelized. This means that the actual work is executed by a high number of contributors in a decentralized fashion; 5 this not only leads to significant improvements in terms of time of delivery, but also offers a means to cross-check the accuracy of the answers (as each task is typically assigned to more than one person). Collecting answers from different workers allow for techniques such as majority voting (or other aggregation methods) to automatically identify accurate responses. The most common reward model in microtask crowdsourcing implies small monetary payments for each worker who has successfully solved a task." ;
    a doco:Paragraph .
}
sub:provenance {
  sub:assertion prov:hadPrimarySource <http://dx.doi.org/10.3233/SW-160239> ;
    prov:wasAttributedTo <https://orcid.org/0000-0003-0530-4305> .
}
sub:pubinfo {
  this: dc:created "2019-11-07T12:47:11+01:00"^^xsd:dateTime ;
    pav:createdBy <https://orcid.org/0000-0002-7114-6459> .
}