Reconceptualizing major policy change in the advocacy coalition framework: a discourse network analysis of German pension politics

How does major policy change come about? This article identifies and rectifies weaknesses in the conceptualization of innovative policy change in the Advocacy Coalition Framework. In a case study of policy belief change preceding an innovative reform in the German subsystem of old-age security, important new aspects of major policy change are carved out. In particular, the analysis traces a transition from one single hegemonic advocacy coalition to another stable coalition, with a transition phase between the two equilibria. The transition phase is characterized (i) by a bipolarization of policy beliefs in the subsystem and (ii) by state actors with shifting coalition memberships due to policy learning across coalitions or due to executive turnover. Apparently, there are subsystems with specific characteristics (presumably redistributive rather than regulative subsystems) in which one hegemonic coalition is the default, or the "normal state." In these subsystems, polarization and shifting coalition memberships seem to interact to produce coalition turnover and major policy change. The case study is based on discourse network analysis, a combination of qualitative content analysis and social network analysis, which provides an intertemporal measurement of advocacy coalition realignment at the level of policy beliefs in a subsystem

Discourse network analysis: policy debates as dynamic networks

Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed

Co-authorship networks in Swiss political research

Co-authorship is an important indicator of scientific collaboration. Co-authorship networks are composed of sub-communities, and researchers can gain visibility by connecting these insulated subgroups. This article presents a comprehensive co-authorship network analysis of Swiss political science. Three levels are addressed: disciplinary cohesion and structure at large, communities, and the integrative capacity of individual researchers. The results suggest that collaboration exists across geographical and language borders even though different regions focus on complementary publication strategies. The subfield of public policy and administration has the highest integrative capacity. Co-authorship is a function of several factors, most importantly being in the same subfield. At the individual level, the analysis identifies researchers who belong to the “inner circle” of Swiss political science and who link different communities. In contrast to previous research, the analysis is based on the full set of publications of all political researchers employed in Switzerland in 2013, including past publications

Policy forums: Why do they exist and what are they used for?

Policy forums are issue-based intermediary organizations where diverse types of political and societal actors repeatedly interact. Policy forums are important elements of modern governance systems as they allow actors to learn, negotiate, or build trust. They can vary in composition, size, membership logic, and other distinct features. This article lays the foundation of a theory of policy forums based on three interrelated elements: First, it discusses conditions for the formation of a forum and describes the logic of these organizations as one of an asymmetric multipartite exchange. Second, it enumerates the potential set of goals and motivations of participating actors that are fed into this exchange. Third, it proposes eight different dimensions on which policy forums differ and which affect the exchange mechanisms among actors. We claim that empirical work on policy forums should systematically take these elements into account and propose elements of a research agenda

Institutional communication revisited: Preferences, opportunity structures and scientific expertise in policy networks

Information exchange in policy networks is usually attributed to preference similarity, influence reputation, social trust and institutional actor roles. We suggest that political opportunity structures and transaction costs play another crucial role and estimate a rich statistical network model on tie formation in the German toxic chemicals policy domain. The results indicate that the effect of preference similarity is absorbed by other determinants while opportunity structures indeed have to be taken into account. We also find that different types of information exchange operate in complementary, but not necessarily congruent, ways.

A Comparison between Political Claims Analysis and Discourse Network Analysis: The Case of Software Patents in the European Union

The study of policy discourse comprises actor-centered and content-oriented approaches. We attempt to close the gap between the two kinds of approaches by introducing a new methodology for the analysis of political discourse called Discourse Network Analysis. It is based on social network analysis and qualitative content analysis and takes an entirely relational perspective. Political discourse can be analyzed in a dynamic way, and the approach makes previously unobservable cleavage lines and alignments measurable at the actor level, at the level of the contents of a discourse, and a combined layer. We compare discourse network analysis with political claims analysis, a competing method, and apply both methods to the European-level discourse on software patents. Our results demonstrate how an anti-softwarepatent coalition was mobilized and how it gained control over important frames, while the well-organized pro-software-patent discourse coalition was not able to gain sovereignty over the discourse.Software Patents, Intellectual Property Rights, Discourse Network Analysis, Social Network Analysis, Political Discourse, Policy Networks, Public Policy Analysis, Social Movements, Political Claims Analysis

Policy Debates and Discourse Network Analysis: A Research Agenda

Discourse network analysis (DNA) is a combination of network analysis and qualitative content analysis. DNA has been applied to various policy processes and debates to show how policy actors are related at the discursive level, complementing coordination relations among them that are often analysed in the application of the policy networks approach. This editorial takes stock of the theoretical and methodological research frontiers in DNA and summarises the contributions of the eleven articles in the thematic issue on “Policy Debates and Discourse Network Analysis” in Politics and Governance

Coping with creeping catastrophes: national political systems and the challenge of slow-moving policy problems

No abstract available

texreg: conversion of statistical model output in R to LaTeX and HTML tables

A recurrent task in applied statistics is the (mostly manual) preparation of model output for inclusion in LaTeX, Microsoft Word, or HTML documents – usually with more than one model presented in a single table along with several goodness-of-fit statistics. However, statistical models in R have diverse object structures and summary methods, which makes this process cumbersome. This article first develops a set of guidelines for converting statistical model output to LaTeX and HTML tables, then assesses to what extent existing packages meet these requirements, and finally presents the texreg package as a solution that meets all of the criteria set out in the beginning. After providing various usage examples, a blueprint for writing custom model extensions is proposed