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ABSTRACTThe traditional process of peer review and publication has come under intense scrutiny in recent years. The time seems propitious for a consideration of alternatives in political science. To that end, we propose a Peer Review and Publication Consortium. The Consortium retains the virtues of the traditional peer-review process (governed by academic journals) while also mitigating some of its vices.

The internet provides a powerful tool to terror organizations, enhancing their public messaging, recruitment ability, and internal communication. In turn, governments have increasingly moved to disrupt terror organizations' internet communications, and even democracies now routinely work to censor terrorist propaganda, and related political messaging, in the name of national security. We argue that democratic states respond to terror attacks by increasing internet censorship and broadening their capacity to limit the digital dissemination of information. This article builds on previous work suggesting this relationship, substantially improving measurement and estimation strategy. We use latent variable modeling techniques to create a new measure of internet censorship, cross nationally and over time, from internet firm transparency reports, and compare this measure to an expert-survey based indicator. Leveraging both measures, we use a variety of panel specifications to establish that, in democracies, increases in terror predict surges in digital censorship. Finally, we examine the posited relationship using synthetic control methods in a liberal democracy that experienced a large shock in terror deaths, France, showing that digital censorship ramped up after several large terrorist attacks.

The expansion of digital interconnectivity has simultaneously increased individuals' access to media and presented governments with new opportunities to regulate information flows. As a result, even highly democratic countries now issue frequent censorship and user data requests to digital content providers. We argue that government internet censorship occurs, in part, for political reasons, and seek to identify the conditions under which states censor. We leverage new, cross-nationally comparable, censorship request data, provided by Google, to examine how country characteristics co-vary with governments' digital censorship activity. Within democracies, we show that governments engage in more digital censorship when internal dissent is present and when their economies produce substantial intellectual property. But these demand mechanisms are modulated by the relative influence that democratic institutions provide to narrow and diffuse interests; in particular, states with proportional electoral institutions censor less.

AbstractModels for converting expert-coded data to estimates of latent concepts assume different data-generating processes (DGPs). In this paper, we simulate ecologically valid data according to different assumptions, and examine the degree to which common methods for aggregating expert-coded data (1) recover true values and (2) construct appropriate coverage intervals. We find that the mean and both hierarchical Aldrich–McKelvey (A–M) scaling and hierarchical item-response theory (IRT) models perform similarly when expert error is low; the hierarchical latent variable models (A-M and IRT) outperform the mean when expert error is high. Hierarchical A–M and IRT models generally perform similarly, although IRT models are often more likely to include true values within their coverage intervals. The median and non-hierarchical latent variable models perform poorly under most assumed DGPs.

Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.

AbstractThe international community spends significant sums of money on democracy promotion, focusing especially on producing competitive and transparent electoral environments. In theory, aid empowers a variety of actors, increasing competition and government responsiveness. We argue that to fully understand the effect of aid on democratization one must consider how democracy aid affects specific country institutions. Building on theory from the democratization and democracy promotion literature, we specify more precise causal linkages between democracy assistance and elections. Specifically, we hypothesize about the effects of democracy aid on the implementation and quality of elections. Building on canonical work, we test these hypotheses, using V‐Dem's detailed elections measures to examine the impact of democracy aid. Intriguingly, we find that there is no consistent relationship between democracy and governance aid and the improvement of disaggregated indicators of election quality, but aggregate measures still capture a relationship. We suggest that current evidence is more consistent with election‐enhancing aid following democratization than with democratization following such aid.

Students of party organization often rely on politicians' perceptions when measuring internal party institutions and organizational characteristics. We compare a commonly used survey measure of political parties' European Parliament candidate selection mechanisms to measures that the authors coded directly from parties' selection rules. We find substantial disconnect between formal institutions and survey respondent perceptions of selection mechanisms, raising questions about measure accuracy and equivalency. While this divergence may be driven either by distinctions between de jure and de facto selection procedures or by respondent error, we find the differences between the two measures are unsystematic. Our findings suggest that authors studying party characteristics must decide whether their research question calls for survey or formal institutional measures.

Using a Bayesian latent variable approach, we synthesize a new measure of democracy, the Unified Democracy Scores (UDS), from 10 extant scales. Our measure eschews the difficult—and often arbitrary—decision to use one existing democracy scale over another in favor of a cumulative approach that allows us to simultaneously leverage the measurement efforts of numerous scholars. The result of this cumulative approach is a measure of democracy that, for every country-year, is at least as reliable as the most reliable component measure and is accompanied by quantitative estimates of uncertainty in the level of democracy. Moreover, for those who wish to continue using previously existing scales or to evaluate research performed using those scales, we extract information from the new measure to perform heretofore impossible direct comparisons between component scales. Specifically, we estimate the relative reliability of the constituent indicators, compare the specific ordinal levels of each of the existing measures in relationship to one another and assess overall levels of disagreement across raters. We make the UDS and associated parameter estimates freely available online and provide a detailed tutorial that demonstrates how to best use the UDS in applied work.

A key obstacle to measurement is the aggregation problem. Where indicators tap into common latent traits in theoretically meaningful ways, the problem may be solved by applying a data-informed ("inductive") measurement model, for example, factor analysis, structural equation models, or item response theory. Where they do not, researchers solve the aggregation problem by appeal to concept-driven ("deductive") criteria, that is, aggregation schemes that do not presume patterns of covariance across observable indicators. This article introduces a novel approach to scale construction that builds on the properties of concepts to solve the aggregation problem. This is accomplished by regarding conceptual attributes as necessary-and-sufficient conditions arrayed in an ordinal scale. While different sorts of scales are useful for different purposes, we argue that "lexical" scales are in many cases superior for research questions where it is relevant to combine the differentiation of an ordinal scale with the distinct, meaningful categories of a typology.

This study assesses how political parties' candidate selection strategies influence women's descriptive parliamentary representation. Focusing on proportional elections, it explores what determines whether parties place women in viable list positions. Evaluating party rankings at the individual level, it directly examines a mechanism – party nomination – central to prevailing explanations of empirical patterns in women's representation. Moreover, it jointly evaluates how incumbency and gender affect nomination. This study uses European Parliament elections to compare a plethora of parties, operating under numerous institutions, in the context of a single legislature. It finds that gender differences in candidate selection are largely explained by incumbency bias, although party ideology and female labor force participation help explain which parties prioritize the placement of novice women.