k-best feature selection and ranking via stochastic approximation

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buir.contributor.authorMalekipirbazarı, Milad
buir.contributor.orcidMalekipirbazari, Milad|0000-0002-3212-6498
dc.citation.volumeNumber213
dc.contributor.authorAkman, David V.
dc.contributor.authorMalekipirbazarı, Milad
dc.contributor.authorYenice, Zeren D.
dc.contributor.authorYeo, Anders
dc.contributor.authorAdhikari, Niranjan
dc.date.accessioned2024-03-14T11:51:33Z
dc.date.available2024-03-14T11:51:33Z
dc.date.issued2023-08-01
dc.departmentDepartment of Industrial Engineering
dc.description.abstractThe relevance and impact of political scientists’ professional activities outside of universities has become the focus of public attention, partly due to growing expectations that research should help address society’s grand challenges. One type of such activity is policy advising. However, little attention has been devoted to understanding the extent and type of policy advising activities political scientists engage in. This paper addresses this gap by adopting a classifcation that distinguishes four ideal types of policy advisors representing difering degrees of engagement. We test this classifcation by calculating a multi-level latent class model to estimate key factors explaining the prevalence of each type based on an original dataset obtained from a survey of political scientists across 39 European countries. Our results challenge the wisdom that political scientists are sitting in an “ivory tower”: the vast majority (80%) of political scientists in Europe are active policy advisers, with most of them providing not only expert guidance but also normative assessments.
dc.description.provenanceMade available in DSpace on 2024-03-14T11:51:33Z (GMT). No. of bitstreams: 1 k-best_feature_selection_and_ranking_via_stochastic_approximation.pdf: 1958521 bytes, checksum: c76843b739d1bcc8fca4c189daa33246 (MD5) Previous issue date: 2023-08-01en
dc.embargo.release2025-08-01
dc.identifier.doi10.1016/j.eswa.2022.118864
dc.identifier.eissn1873-6793
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/11693/114752
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.isversionofhttps://doi.org/10.1016/j.eswa.2022.118864
dc.rightsCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleExpert Systems with Applications
dc.subjectEuropean political science
dc.subjectPolicy advisors
dc.subjectLatent class analysis
dc.titlek-best feature selection and ranking via stochastic approximation
dc.typeArticle

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