We rely on a novel database of Spanish author-inventors to explore the relationship between the past patenting experience of academic authors and the scientific impact (citations received and journal prestige) of scientific articles published during [2003][2004][2005][2006][2007][2008] in journals listed in SCOPUS. We also study how such a relationship is affected by differences across academic affiliations, distinguishing between public universities and different types of non-university public research organisations. Our econometric estimations show that scientific impact is positively associated with having authors with past patenting experience as inventors at the European Patent Office. Exceptions are the articles of authors affiliated to new independent public research centres, not tied to the civil service model and oriented to do research that is both excellent and use-inspired. These are also on average the most cited articles.
The aim of this paper is to describe a matching and disambiguation methodology for the identification of author-inventors located in the same country. It aims to maximize precision and recall rates by taking into account national name writing customs in the name matching stage and by including a recursive validation step in the person disambiguation stage. An application to the identification of Spanish author-inventors is described in detail, where all SCOPUS 2003-2008 publications of Spanish authors are matched to all 1978-2009 EPO applications with Spanish inventors. Using this data, we identify 4,194 Spanish author-inventors. A first look at their patenting and publication patterns reveal that Spanish author-inventors make quite a significant contribution to the overall country's scientific and technological production in the time periods considered: 27% of all EPO patent applications invented in Spain and 15% of all SCOPUS scientific articles authored in Spain, with important differences across fields and excluding journals in non-technologically relevant fields.
We explore how microwork platforms manage difficult tasks in paid crowdsourcing environments. We argue that as human computation becomes more prevalent, notably in the context of big data ecosystems, microwork platforms might have to evolve and to take a more managerial stance in order to provide the right incentives to online workers to handle difficult tasks. We illustrate this first through a name disambiguation experiment on Amazon Mechanical Turk (AMT), a well-known microwork platform, and second through direct analysis of the dynamics of task execution in a dataset of real microwork projects on AMT. We discuss the emergence of more specialised microwork platforms as an attempt to facilitate a better management of difficult tasks in the context of paid crowdsourcing.
We explore how microwork platforms manage difficult tasks in paid crowdsourcing environments. We argue that as human computation becomes more prevalent, notably in the context of big data ecosystems, microwork platforms might have to evolve and to take a more managerial stance in order to provide the right incentives to online workers to handle difficult tasks. We illustrate this first through a name disambiguation experiment on Amazon Mechanical Turk (AMT), a well-known microwork platform, and second through direct analysis of the dynamics of task execution in a dataset of real microwork projects on AMT. We discuss the emergence of more specialised microwork platforms as an attempt to facilitate a better management of difficult tasks in the context of paid crowdsourcing.
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