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Using Computational Text Mining to Understand Public Priorities for Disability Policy Towards Children in Canadian National Consultations

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Title:Using Computational Text Mining to Understand Public Priorities for Disability Policy Towards Children in Canadian National Consultations
Authors:Cogburn, Derrick
Shikako-Thomas, Keiko
Lai, Jonathan
Keywords:Text Analytics
categorization models
crc
crpd
public policy consultations
show 1 moretext mining
show less
Date Issued:07 Jan 2020
Abstract:Identifying policy preferences from public consultations presents a challenge to national and local governments. Computational text mining approaches provide a useful strategy for analyzing the large-scale textual data emerging from these policy processes. In this study, we developed an inductive and deductive text mining approach to understand disability-related policy priorities. This approach is applied to data from the nationwide disability policy consultation conducted in 2016 by the Government of Canada. This process included 18 town hall meetings, 9 thematic roundtables, and online submissions from 92 stakeholders. Transcripts of these consultations were made available to researchers. Three broad research questions were asked of this data, focused on key themes; differences by city size and type of consultation; and impact of two global policy frameworks. The study identified a number of key themes and saw differences by city size. The study identified content related to both the CRPD and CRC.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63835
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.096
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Text Analytics


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