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Assessing the prominence of interest groups in parliament: a supervised machine learning approach

Fraussen, Bert; Graham, Timothy; Halpin, Darren

Description

Ascertaining which interest groups are considered relevant by policymakers presents an important challenge for political scientists. Existing approaches often focus on the submission of written evidence or the inclusion in expert committees. While these approaches capture the effort of groups, they do not directly indicate whether policy makers consider these groups as highly relevant political actors. In this paper we introduce a novel theoretical approach to address this important question,...[Show more]

dc.contributor.authorFraussen, Bert
dc.contributor.authorGraham, Timothy
dc.contributor.authorHalpin, Darren
dc.date.accessioned2019-06-18T04:10:52Z
dc.date.available2019-06-18T04:10:52Z
dc.identifier.issn1357-2334
dc.identifier.urihttp://hdl.handle.net/1885/164089
dc.description.abstractAscertaining which interest groups are considered relevant by policymakers presents an important challenge for political scientists. Existing approaches often focus on the submission of written evidence or the inclusion in expert committees. While these approaches capture the effort of groups, they do not directly indicate whether policy makers consider these groups as highly relevant political actors. In this paper we introduce a novel theoretical approach to address this important question, namely prominence. We argue that, in the legislative arena, prominence can be operationalised as groups being mentioned strategically – used as a resource – by elected officials as they debate policy matters. Furthermore, we apply a machine learning solution to reliably assess which groups are prominent among legislators. We illustrate this novel method relying on a dataset of mentions of over 1300 national interest groups in parliamentary debates in Australia over a six-year period (2010–2016).
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherTaylor & Francis
dc.rights© 2018 The Author(s).
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceThe Journal of Legislative Studies
dc.titleAssessing the prominence of interest groups in parliament: a supervised machine learning approach
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume24
dc.date.issued2018
local.identifier.absfor160899 - Sociology not elsewhere classified
local.identifier.absfor160699 - Political Science not elsewhere classified
local.identifier.ariespublicationu3102795xPUB111
local.publisher.urlhttps://www.routledge.com/
local.type.statusPublished Version
local.contributor.affiliationFraussen, Bert, Leiden University
local.contributor.affiliationGraham, Timothy, College of Arts and Social Sciences, ANU
local.contributor.affiliationHalpin, Darren, College of Arts and Social Sciences, ANU
dc.relationhttp://purl.org/au-research/grants/arc/DP140104097
local.bibliographicCitation.issue4
local.bibliographicCitation.startpage450
local.bibliographicCitation.lastpage474
local.identifier.doi10.1080/13572334.2018.1540117
local.identifier.absseo940299 - Government and Politics not elsewhere classified
dc.date.updated2019-03-24T07:18:29Z
local.identifier.scopusID2-s2.0-85057260820
dcterms.accessRightsOpen Access
dc.provenancePublished by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
dc.rights.licenseCreative Commons Attribution-NonCommercial-NoDerivatives License
CollectionsANU Research Publications

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