Kristian Skrede Gleditsch

This data feature presents an update (Version 4.0) to the Non-State Actor (NSA) data, which provide information on the characteristics of rebel groups in all state-rebel group dyads in the Uppsala Conflict Data Program/Peace Research Institute Oslo Armed Conflict Dataset (ACD). The previous version of the NSA data ended in 2011, and Version 4.0 extends coverage through 2024, adding more than 250 new dyad spells and aligning pre-2011 data with Version 25.1 of the ACD. The added period is a consequential one for the study of conflict: the number of conflicts and conflict fatalities have risen substantially, while international conflict management efforts have declined. We describe the coding process and present descriptive analyses showing marked increases in rebel strength relative to the government and in rebel territorial control over the post-2011 period. We also discuss applications of the data to research on rebel governance and violence against civilians, two areas where scholarship has grown substantially since the data were last updated. The update further allows the range of data projects built on the NSA to be extended, supporting research on how the changing character of contemporary conflict shapes its dynamics and outcomes.

Although very large wars remain an enduring threat in global politics, we lack a clear understanding of how some wars become large and costly, while most do not. There are three possibilities: Large conflicts begin with intense fighting, accumulate severity over a long duration, or escalate in intensity over time. Using detailed within-conflict data on civil and interstate wars (1946–2008), we show that escalation dynamics—variations in fighting intensity within an armed conflict—are the primary mechanism for producing large conflicts. Civil wars, however, tend to deescalate once they become very large, limiting their overall severity, while interstate wars exhibit a persistent risk of continual escalation. Simple models of within-conflict severity dynamics show that this distinction compactly explains the historical size distributions of civil and interstate wars, and unconditioned escalation is a plausible mechanism for Richardson’s law—the power law pattern in the frequency and severity of interstate conflicts. The dynamics of escalation within conflicts have broad implications for conflict theory and risk assessment.

Mass mobilization is widely regarded as a key driver of democratic change, yet concerns about its declining impact are growing. We examine how the effectiveness of mobilization in prompting democratic transitions has varied over the period 1900–2019 using formal statistical models. The results reveal pronounced temporal heterogeneity: mobilization has a positive and significant effect on democratic transitions from the early 1980s to the mid-2010s, but no detectable impact before or after. Leader turnover and coup attempts have been more likely to follow mobilization up to the Arab Spring, yet the increasing impact of mobilization after the 1980s is unique to democratization. Trends in commonly cited structural conditions for democracy do not align with the observed variation in mobilization’s effectiveness. These findings challenge assumptions of time-invariant effects and highlight the importance of accounting for temporal variation in processes of democratization and political change.

The 60th anniversary of the Department of Government at the University of Essex provides an opportunity to reflect on its many achievements and why these have been possible. This article argues that research excellence is a collective outcome that cannot be reduced to individuals. Research institutions tend to be successful because they manage to create productive environments, which can make individual scholars better and create synergies. The thesis is backed up by examples from the history of the department and more general research on the role of environments for research. The article considers possible insights with regard to present challenges to academic institutions, why productive environments can be difficult to maintain, and how we can try to nurture them.

Governmental and nongovernmental organizations have increasingly relied on early-warning systems of conflict to support their decisionmaking. Predictions of war intensity as probability distributions prove closer to what policymakers need than point estimates, as they encompass useful representations of both the most likely outcome and the lower-probability risk that conflicts escalate catastrophically. Point-estimate predictions, by contrast, fail to represent the inherent uncertainty in the distribution of conflict fatalities. Yet, current early warning systems are preponderantly focused on providing point estimates, while efforts to forecast conflict fatalities as a probability distribution remain sparse. Building on the predecessor VIEWS competition, we organize a prediction challenge to encourage endeavours in this direction. We invite researchers across multiple disciplinary fields, from conflict studies to computer science, to forecast the number of fatalities in state-based armed conflicts, in the form of the UCDP ‘best’ estimates aggregated to two units of analysis (country-months and PRIO-GRID-months), with estimates of uncertainty. This article introduces the goal and motivation behind the prediction challenge, presents a set of evaluation metrics to assess the performance of the forecasting models, describes the benchmark models which the contributions are evaluated against, and summarizes the salient features of the submitted contributions.

S: This research note introduces a new dataset—the Governmental Incompatibilities Data Project (GIDP) 2.0—which identifies the presence of incompatibilities over governments for all countries in the world from 1960 to 2020. Incompatibilities over government involve organizations making maximalist claims related to the legitimacy of elections, the composition of the national government, or regime change. GIDP 2.0 includes information about which of these claims is present in each incompatibility year. These data can facilitate analyses of the onset, dynamics, and outcomes of both civil war and nonviolent campaigns, improve our ability to predict their occurrence, and allow for analysis of whether international efforts to prevent violent conflicts over government are effective. We present a series of descriptive analyses showing that governmental incompatibilities are common but not ubiquitous, and occur across time periods, and within and across regime types. These descriptive analyses further show interesting variation among the types of claims articulated in democracies, autocracies, and anocracies and across different types of autocratic institutions. A brief two-stage analysis shows that some factors commonly included in studies of armed conflict and nonviolent campaign onset have different effects on the emergence of governmental incompatibilities and on whether these incompatibilities escalate to mass mobilization.

We argue that community deprivation can increase the risk of right-wing radicalization and violent attacks and that measures of local deprivation can help improve forecasting local hate crime rates. A large body of research stresses how experiences of deprivation can erode the perceived legitimacy of political leaders and institutions, increase alienation, and encourage right-wing radicalization and hate crime. Existing analyses have found limited support for a close relationship between deprivation and radicalization among individuals. We provide an alternative approach using highly disaggregated data for England and show that information on local deprivation can improve predictions of the location of right-wing hate crime attacks. Beyond the ability to predict where right-wing hate crime is likely, our results suggest that efforts to decrease deprivation can have important consequences for political violence, and that targeting structural facilitators to prevent far-right violence ex ante can be an alternative or complement to ex post measures.

The COVID 19 pandemic has generated much interest in the relationship between research and policy. It has drawn new attention to the limitations of a linear model, where policy is based on first observing prior scientific research and then designed in response to this. Conflict researchers often motivate the importance of their work by claiming that their “research has important policy implications”, but the proposals offered are often at best incomplete. I identify a number of common limitations in claims about policy implications, including a lack of discussion of objectives and priorities, stating objectives themselves as if they were policies, claims about targeting factors without discussing the effectiveness of possible interventions, and a failure to consider uncertainty and potential tensions with other objectives or unintended effects. Research can potentially inform policy discussions and improve decisions, but the incentives in academic research are very different from policy decisions, and the latter often calls for very different evidence than what is offered by the former. Rather than attempting to offer policy prescriptions as an afterthought to academic articles, research can be more helpful to policy by trying to inform debates, focusing on what we know from the cumulative body of research than individual manuscripts, and providing new data and empirical material that allow for better problem description and analysis.