Slava Mikhaylov

The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health

The Lancet Countdown tracks progress on health and climate change and provides an independent assessment of the health effects of climate change, the implementation of the Paris Agreement, 1 and th ...

Crowd-sourced text analysis: reproducible and agile production of political data

Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of nonexperts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers’ attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.

Natural Sentences as Valid Units for Coded Political Texts

All methods for analyzing text require the identification of a fundamental unit of analysis. In expert-coded content analysis schemes such as the Comparative Manifesto Project, this unit is the ‘quasi-sentence’: a natural sentence or a part of a sentence judged by the coder to have an independent component of meaning. Because they are subjective constructs identified by individual coders, however, quasi-sentences make text analysis fundamentally unreliable. The justification for quasi-sentences is a supposed gain in coding validity. We show that this justification is unfounded: using quasi-sentences does not produce valuable additional information in characterizing substantive political content. Using natural sentences as text units, by contrast, delivers perfectly reliable unitization with no measurable loss in content validity of the resulting estimates.

A Conservative Revolution: The Electoral Response to Economic Crisis in Ireland

Abstract The 2011 election in Ireland was one of the most dramatic elections in European post-war history in terms of net electoral volatility. In some respects the election overturned the traditional party system. Yet it was a conservative revolution, one in which the main players remained the same, and the switch in the major government party was merely one in which one centre-right party replaced another. Comparing voting behaviour over the last three elections we show that the 2011 election looks much like that of 2002 and 2007. The crisis did not result in the redefinition of the electoral landscape. While we find clear evidence of economic voting at the 2011 election, issue voting remained weak. We believe that this is due to the fact that parties have not offered clear policy alternatives to the electorate in the recent past and did not do so in 2011.

Understanding state preferences with text as data: Introducing the UN General Debate corpus:

Every year at the United Nations (UN), member states deliver statements during the General Debate (GD) discussing major issues in world politics. These speeches provide invaluable information on governments’ perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7300 country statements from 1970–2014. We demonstrate how the UN GD corpus (UNGDC) can be used as a resource from which country positions on different policy dimensions can be derived using text analytic methods. The article provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

Big Data and AI – A transformational shift for government: So, what next for research?:

Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed.