Incrementalism in Appropriations: Small Aggregation,Big Changes

U.S. federal budget dynamics, as a major attribute of the legislative and bureaucratic decision‐making processes, increasingly calls into question the scholarly focus on incrementalism. What constitutes a “small” change is largely unspecified in previous research that has also been unable to assess incrementalism across multiple levels of aggregation. Using a unique budgetary database, this article analyzes whether budgetary changes are in fact “small” at different levels of aggregation. Surprisingly, a low proportion of changes are small by any logical standard. During most years, more than one‐fifth of budgetary changes are greater than 50 percent, and nearly half are more than 10 percent. The level of aggregation is also important for assessing whether political variables influence incrementalism. A salient finding: change in party control reflects greater influence within micro‐level budget decisions, while divided government manifests more impact on aggregate‐level budget decisions.     

大数据、权利实现与基层治理创新

治理的本质是实现权利。以权利为媒介,大数据与治理形成一种间接的因果关系,干预并驱动了基层治理的创新。依循"合作+冲突"的基本逻辑,按照权利的合作、权力的冲突、权利联合权力的三种途径,大数据从价值、技术和资源等三个维度,驱动着公民权利的实现。其中,权利合作表现为一种"差序格局",权力冲突表现为制衡与监察,"合作"权利与"冲突"权力的合作则正在成为一项基本选择。而要提升大数据驱动基层治理创新的效率,需要推动基层政务大数据的开放、共享,完善大数据治理的配套法律法规,培育一个成熟的公民群体。     

Big Societies,Little Platoons and the Problems with Pluralism

Conservative talk of a ‘big society’ is one more mutation of the unstable family of pluralism. It is a large family, with both benign and progressive left wing members and rogue right wing black sheep. The progressive contribution has been substantial, but pluralism is not a homogeneous ideology or set of policies, and the single word conceals stresses and irreconcilable oppositions in theory and practice. An examination of this variety gives clues to the latest contributions, and to some of the more regressive uses to which pluralism can be put. The left needs both to insist on its own major contribution to progressive pluralism, and to beware of wolves in pluralist clothing.     

The Big (Data) Bang: Policy,Prospects, and Challenges

Big data is increasingly the cornerstone on which policy making is based. However, with potential benefits and applications come challenges and dilemmas. In this set of symposium articles, authors examine the promise and problems of big data, exploring associated prospects, risks, parameters, and payoffs from a variety of perspectives. The articles address myriad challenges in the handling of big data sets, such as collection, validation, integrity, and security; ontological issues attending data analytics and conceptual transformations; the foundations of big data collection for social science research; the gap between the acquisition of data and its use to advance discovery and innovation; the costs and benefits of using big data in decision making and analysis; and, finally, related problems of privacy, security, and ethics. Issues such as these will continue to arise with increased use of big data as fundamental to policy making and governance in today's growing information society.     

Agenda-Building and Big Science

This paper extends concepts about agenda-building politics within the policy literature by considering the implications of `big science' agenda-building politics for policyrnaking. It argues that agenda-building in the `big science' policy arena is distinct from agenda-building in the general social problem-solving policy arena. The general policy development process and the `big science' policy development process are contrasted conceptually. Two different cases of agenda-building in big science are then highlighted to offer empirical evidence of the differences between the two processes. A distinction is drawn between the contemporary conceptualization of agenda-building in the general policy arena and the big science policy arena and comments are made about the value of the paper for policy learning. Accordingly, the paper has important implications for the science policy arena and perhaps the larger policy arena.     

“Big Tigers,Big Data”: Learning Social Reactions to China's Anticorruption Campaign through Online Feedback

This article examines the effect of campaign‐style anticorruption efforts on political support using the case of China's most recent anticorruption drive, which stands out for its harsh crackdown on high‐ranking officials, known as “big tigers.” An exploratory text analysis of more than 370,000 online comments on the downfall of the first 100 big tigers, from 2012 to 2015, reveals that public support for the top national leader who initiated the anticorruption campaign significantly exceeded that afforded to anticorruption agencies and institutions. Further regression analyses show that support for the leaders with respect to intuitions increased with the tigers' party ranking. Findings suggest that while campaign‐style enforcement can reinforce the central authority and magnify support for individual leaders, it may also marginalize the role of legal institutions crucial to long‐term corruption control.     

From Big Data to Knowledge: An Ontological Approach to Big Data Analytics

The introduction of Big Data sets in the healthcare domain has presented opportunities to engage in analytics of very large sets containing both structured and unstructured data. With advances in information technology (IT), these data sets have become available from diverse sources at greatly increased rates. The availability of Big Data sets has introduced complexities that we must address, not only in terms of semantics and analytics but also in terms of data management, storage, and distribution. Currently, the capabilities to ingest, analyze, and manage multipetabyte data sets have underscored the limitations of our analytics capabilities supported by relational database management systems. This essay argues that an ontology‐based approach to data analytics provides a practical framework to address the semantic challenges presented by Big Data sets. No ontological framework can address the operational and management requirements introduced by the availability of Big Data sets, however. There are also a number of IT architectural factors that must be considered in implementing such a framework.     

Subnational Debt Swaps in Mexico,How Big Is Risk Exposure?

Research and practice with derivative instruments by States and Municipalities in Mexico are virtually unexplored. This paper investigates the effect of interest rate jumps and financial contagion on the risk exposure of Subnational Government debt swaps in Mexico. Unconditional risk exposure measures of debt swaps are obtained by simulating a set of stochastic processes with jumps. Simulation results confirm that risk exposure of local debt swaps increases with Poisson discontinuities while a significant effect is observed after crisis contagion is accounted for.     

Big Data for Policy Analysis: The Good,The Bad,and The Ugly

Big data holds tremendous potential for public policy analysis. At the same time, its use prompts a number of issues related to statistical bias, privacy, equity, and governance, among others. Accordingly, there is a need to formulate, evaluate, and implement policies that not only mitigate the risks, but also maximize the benefits of using big data for policy analysis. This poses a number of challenges, which are highlighted in this essay.     

Big Data and Strategic Intelligence

This article examines the intersection of Big Data and strategic intelligence from a theoretical-conceptual viewpoint. Adopting Popperian refutation as a starting point, it approaches methodological issues surrounding the incorporation of Big Data into the intelligence cycle, and argues that Big Data analytics is best used to discern long-term developments, generate intelligence hypotheses, and adduce refuting facts. The article then briefly examines the use of Big Data via social media, an increasingly fertile platform for intelligence analysis. Finally, the article argues that despite its potential in filling our epistemic gaps, Big Data should continue to complement traditional subject-matter expertise, supported by game theory, as part of a tripartite analytical framework for strategic intelligence consisting of ‘subtext’, ‘context’ and ‘metatext’. In this respect, Big Data may well become the midwife for more open modes of intelligence management and, ultimately, a more open society.