When does open government shut? Predicting government responses to citizen information requests
When does open government shut? Predicting government responses to citizen information requests
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开放政府什么时候关闭？
DOI：
10.1111/rego.12282
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发表时间：
2019
影响因子：
3
通讯作者：
Zack W. Almquist
中科院分区：
文献类型：
--
作者：
Benjamin E. Bagozzi;Daniel Berliner;Zack W. Almquist
公民与政府互动的“大数据”分析方法对于官僚反应的理论评估是必要的。这种大数据方法也有利于从业者监控和改进这些新兴透明度机制的能力。我们认为有监督的潜在狄利克雷分配（sLDA）是实现这些目的的潜在方法。为此，我们使用 sLDA 来检查墨西哥政府对 2003 年至 2015 年期间向墨西哥联邦政府提交的所有公共信息请求的（不）响应情况，并确定与（不）响应最相关的请求主题。从本质上讲，我们对最能预测回应性和不回应性的主题的比较表明，政治敏感性在塑造这一领域的官方行为方面发挥着巨大而重要的作用。因此，我们得出的结论是，sLDA 为分析（i）公民与政府互动的文本记录和（ii）官僚对这些互动的（非）反应性的分析提供了独特的优势和见解。
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.