A Relative Impact Ranking of Political Studies In Ireland

Against a background of the Irish government’s concerns with Key Performance Indicators (KPIs) and the British government’s wishes for a more quantitative Research Assessment Exercise (RAE), our study conducts a relative impact assessment of the study of politics, government, political science, and international relations in Ireland. Impact is measured as citations from the publications of permanent staff in eight Irish politics departments, based on data compiled in April 2008 from two leading academic indexes – ISI’s Web of Science and Scopus – as well as the now popular Google Scholar. We discuss some of the criticisms that naturally arise in a study of this nature. Then, following similar exercises in other disciplines (e.g. economics), we use the impact measures to compare and rank individual scholars as well as departments. We also explore the extent to which the choice of different indexes, and different measures, influences the results that we obtain. While there are differences, in particular between indexes based purely on articles and those that access books and other material, the results from the different indexes are strongly correlated.

The most unkindest cuts: speaker selection and expressed government dissent during economic crisis

Economic crisis and the resulting need for austerity budgets have divided many governing parties and coalitions in Europe, despite strong party discipline in the legislative voting on these harsh budgets. We measure these divisions using automated text analysis methods to scale the positions that legislators express in budget debates, in an effort to avoid punishment by voters for supporting austerity measures, while still adhering to strict party discipline by voting along party lines. Our test case is Ireland, a country that has experienced both periods of rapid economic growth as well as one deep financial and economic crisis. Tracking dissent from 1987 to 2013, we show that austerity measures undermine government cohesion, as verbal opposition markedly increases in direct response to the economic pain felt in a legislator’s constituency. The economic vulnerability of a legislator’s constituency also directly explains position taking on austerity budgets among both government and opposition

Estimating intra-party preferences: comparing speeches tovotes

Well-established methods exist for measuring party positions, but reliable means for estimating intra-party preferences remain underdeveloped. While most efforts focus on estimating the ideal points of individual legislators based on inductive scaling of roll call votes, this data suffers from two problems: selection bias due to unrecorded votes, and strong party discipline which tends to make voting a strategic rather than a sincere indication of preferences. By contrast, legislative speeches are relatively unconstrained, since party leaders are less likely to punish MPs for speaking freely as long as they vote with the party line. Yet the differences between roll call estimations and text scalings remain essentially unexplored, despite the growing application of statistical analysis of textual data to measure policy preferences. Our paper addresses this lacuna by exploiting a rich feature of the Swiss legislature: On most bills, legislators both vote and speak many times. Using this data, we compare text-based scaling of ideal points to vote-based scaling from a crucial piece of energy legislation. Our findings confirm that text scalings reveal larger intra-party differences than roll calls. Using regression models we further explain the differences between roll call and text scalings by attributing differences to constituency-level preferences for energy policy

More positive, assertive and forward-looking: how Leave won Twitter

How did people talk about the EU referendum on Twitter? Akitaka Matsuo and Kenneth Benoit (left) analysed 23m tweets about Brexit, and found salient differences between Leave and Remain supporters. People who backed Leave were more likely to use positive, assertive and forward-looking language. They also tended to follow politicians and campaigning accounts, while Remain supporters were more likely to follow journalists. Overall, Leave were in a better position on Twitter

Spain and European Union Constitution-building

The broad objective of this paper is to better understand how national governments form their policy position on the Draft Constitutional proposal by analysing developments in Spain. It examines the Spanish position on various parts of the European Convention based on data from expert interviews carried out in November 2003. It then evaluates the internal coordination process, focusing on the few domestic-level actors (from the Prime Ministers’ Office and the Ministries of Foreign Affairs and Economy) that have been most influential in shaping Spain’s position. Finally, it ponders the potential shift in the Spanish position given the recent Socialist victory in March 2004

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 non-experts, 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

Introduction to the Special Issue: An Overview of the Gulf of Mexico Research Initiative

Prior to the Deepwater Horizon (DWH) incident on April 20, 2010, in the Gulf of Mexico, the state of knowledge concerning oil in the sea was well summarized by the third National Research Council report (National Research Council, 2003). Since that report was published, several ongoing studies have examined spills in cold and shallow waters, for example, Peterson et al. (2003) and Wiens (2013) on the legacies and lessons of the 1989 Exxon Valdez oil spill. Oil exploration and production has moved further offshore and into much deeper water in recent decades. The DWH/Macondo blowout occurred in water over 1,000 m deep, in a relatively warm near-surface water environment, and in a region where naturally occurring seeps of oil are also common. Despite ongoing general oceanographic research in the Gulf of Mexico, establishment of ocean observing systems, and several programs funded by the US Bureau of Ocean Energy Management (BOEM, formerly Minerals Management Service), prior knowledge of oceanography in the Gulf proved to be inadequate, and not fully appropriate, for this unprecedented event, as observations in the vicinity of the spill rapidly demonstrated (see Overton et al. and Passow and Hetland in this issue). Major environmental events like the DWH spill trigger a legal process called Natural Resource Damage Assessment (NRDA) that brings together federal agencies, states, and Native American tribes to evaluate the impacts of the event on natural resources, in this case, along the nation’s coast. Because there were legal and procedural constraints on resulting field programs, data collection, and other research by US federal government agencies and their contractors as well as on BP investigations, a major program of independent scientific investigations was urgently needed. Fortunately, BP quickly established the Gulf of Mexico Research Initiative (GoMRI) to address this knowledge deficit, and GoMRI has been able to support unfettered and independent research (see Colwell). This article provides an overview of the science undertaken by the GoMRI program and its management

AI and data science for public policy

Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need for fairness, human judgment, and democratic accountability. The issue provides a roadmap for harnessing AI’s potential responsibly, ensuring it serves the public good and upholds democratic values