When does open government shut? Predicting government responses to citizen information requests

Dados Bibliográficos

ID7366986
AutoresBenjamin E Bagozzi (0000-0002-6233-6453), Daniel Berliner (0000-0002-0285-0215, autor correspondente), Z Almquist (0000-0002-1967-123X)
Ano2019
Volume15
Fascículo2
Páginas280-297
Data de publicação2019-10-21
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoRegulation & Governance (JOURNAL)
Identificadores do periódicoISSN: 1748-5983 • E-ISSN: 1748-5991
EditoraWiley (PUBLISHER • GB)
DOI10.1111/rego.12282
OpenAlexW2982003288
IdiomaEN
Citações recebidas18
Referências citadas43

Methods for the analysis of “big data” on citizen‐government interactions are necessary for theoretical assessments of bureaucratic responsiveness. Such big data methods also stand to benefit practitioners' abilities to monitor and improve these emerging transparency mechanisms. We consider supervised latent Dirichlet allocation (sLDA) as a potential method for these purposes. To this end, we use sLDA to examine the Mexican government's (non)responsiveness to all public information requests filed with the federal Mexican government during the 2003–2015 period, and to identify the request topics most associated with (non)responsiveness. Substantively, our comparisons of the topics that are most highly predictive of responsiveness and nonresponsivess indicate that political sensitivity plays a large and important role in shaping official behavior in this arena. We thus conclude that sLDA provides unique advantages for, and insights into, the analysis of (i) textual records of citizen–government interactions and (ii) bureaucratic (non)responsiveness to these interactions

Big data · Bureaucracy · Business · Data mining · Government (linguistics · Latent Dirichlet allocation · Open government · Political science · Politics · Public relations · Topic model · Transparency (behavior · Computational and Text Analysis Methods · Computer Science · Electoral Systems and Political Participation · Law · Public Administration · Social Media and Politics · Artificial Intelligence