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

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.