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dc.contributor.authorDaniel Behn*
dc.contributor.authorRunar Lie*
dc.contributor.authorMalcolm Langford*
dc.date.accessioned2021-02-12T10:16:44Z
dc.date.available2021-02-12T10:16:44Z
dc.date.issued2020*
dc.date.submitted2021-01-04 11:59:56*
dc.identifier51234*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/63285
dc.description.abstractFeaturing contributions from a diverse set of experts, this thought-provoking book offers a visionary introduction to the computational turn in law and the resulting emergence of the computational legal studies field. It explores how computational data creation, collection, and analysis techniques are transforming the way in which we comprehend and study the law, and the implications that this has for the future of legal studies.*
dc.languageEnglish*
dc.subjectK1-7720*
dc.subject.otherlegal research*
dc.subject.otherempirical legal research*
dc.subject.otherlaw and economics*
dc.subject.othercomputational law book*
dc.subject.othercomputational legal studies*
dc.subject.othercomputational social science*
dc.titleComputational stylometry: predicting the authorship of investment arbitration awards*
dc.typechapter
oapen.identifier.doi10.4337/9781788977456.00008*
oapen.relation.isPublishedBy01ceac28-75b4-492a-8eec-f9b98bc6b28c*
oapen.relation.isPartOfBook9781788977449*
oapen.relation.isPartOfBook9781788977456*


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https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/