Volume 8, Issue 2 - Special Issue on Big Data

The use of big data represents a valuable way to inspire decision-making in a time of scarce resources. The technological revolution is in fact enabling governments to use a great variety of digital tools and data to manage all phases of the policy cycle process, becoming a core element for e-governance applications and techniques. However, research is seemingly not yet aligned yet with the hybrid environment that both public policies and politics are moving in, while the actors (old and new) and the decision-making processes themselves, in their searching for automation and objectivity, risk being overshadowed. Taking the case of Higher Education, this article proposes a research framework for big-data use to prompt the reflection on the power of “evidence” in decision making; to question and contextualize such evidences in a multimodal and integrated scenario, and to understand the challenges that data will pose to education both in terms of unforeseen and hidden effects.

Although Twitter has become a major source of data, research still relies on heavy manual searches to find the accounts of a population of interest, such as members of Parliament, members of an association or candidates to an election. In this article, we propose a method to semi-automatically retrieve those accounts. After creating a pool of possible accounts through automatic web searches, we select a unique account for each member of a defined group. To do so, we use a simplified version of the minimax regret approach, on a score based on the account’s handle and the tweets’ content. We test this method to find the accounts of the candidates for the 2015 Spanish general elections and the members of the 57th House of Commons in the United Kingdom.

This paper contributes to the growing empirical work on deliberation in legislatures by proposing a novel approach to analysing parliamentary hearings using both thematic and topic modelling textual analysis software. We explore variations in deliberative quality across economic policy type (fiscal policy, monetary policy and financial stability) and across parliamentary chambers (Commons and Lords) in UK select committee oversight hearings during the 2010–2015 Parliament. Our overall focus is not only to suggest a multi-method approach to the textual analysis of parliamentary data, but also to explore more substantive aspects of parliamentary oversight, such as: (1) the extent to which oversight varies between unelected and elected policy makers; and (2) whether parliamentarians conduct oversight more forcefully or more along partisan lines when they are challenging fellow politicians as opposed to central bank officials. Our findings suggest consistent differences in deliberative styles between types of hearings (fiscal, monetary, financial stability) and between chambers (Commons, Lords).

Dissenting opinions are common in the US Supreme Court even though they take time and effort, risk infuriating colleagues, and have no precedential value. In spite of these drawbacks, dissents can potentially contribute to future legal development. We theorize that dissenting justices who use more memorable language are more successful in achieving such long-term impact. To test this theory, we amass an original dataset of citations to dissenting opinions extracted directly from majority opinion text. We further leverage these texts to build an algorithm that quantifies the distinctiveness of dissenting language within a dynamic context. Our results indicate that dissents using more negative emotion and more distinctive words are cited more in future majority opinions. These results contribute to our understanding of how language can influence long-term policy development.

Most of the literature on policy diffusion focuses on palpable issues such as economic or morality policies. As such, we know little about the mechanisms of diffusion for preemption of atypical policies such as animal regulations that lack a clear economic or ideological motivation. In this article, we propose and test a theory of conditional policy learning to explain the diffusion of atypical policies. We posit that a type of policy learning is occurring here, but that states only look to their neighbors when certain policy specific factors are present in their state. His theory is then applied to examine the dynamics of state adoption of laws preempting local Breed Specific Legislation from 1988 to 2014. Using an exponential model, two policy learning and two conditional learning hypotheses are tested. This study finds that policy learning is occurring through both external and internal pathways. This advances the literature by demonstrating that preemption occurs through the learning mechanism, but this learning effect is conditioned on policy relevant factors within the state.

This paper investigates the determinants of voter turnout in the 2015 election in Nigeria. Variables drawn from political participation models were tested on the likelihood that they influence turnout. The paper finds that political trust and home ownership had a significant impact on participation more than other factors. However, it did not underestimate the influence socio-economic variables can have on voter turnout especially how they can sway the effect of political trust on individuals’ decision to turnout.