A statistical procedure for representing state fragility and transition paths

State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.     

Systemic risk statistics with scenario analysis

In this paper, we introduce two new classes of risk statistics, named convex and positively homogeneous systemic risk statistics, respectively. Structural decomposition results and representation results for them are provided. These new risk statistics can be considered as a kind of systemic risk extension of risk statistics introduced by Kou, Peng, and Heyde, and also empirical versions of system risk measures introduced by Cehn, Iyengar, and Mollemi and Kromer, Overbeck, and Zich. Finally, some examples are also given.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

Inclusive education policy provision in South African research universities

The 4-A Framework suggests that all inclusive education systems must be available, accessible, acceptable and adaptable. South Africa is committed to establishing an inclusive education system that does not exclude students with disabilities. South African university disability policies translate the imperatives of the United Nations Convention on the Rights of Persons with Disabilities into institutional mandates that govern the implementation of inclusive education. The aim of this study was to determine to what extent disability policies at South African research-intensive universities addressed the markers contained in the 4-A Framework. The framework was reworked and operationalised for the higher education context, and expanded to include a fifth marker, namely affordability. A deductive thematic analysis, using a protocol based on the five markers, was used to analyse these policies. Results indicate that, of the five markers, affordability was not sufficiently addressed. Various disclaimers also limit policy provisions related to other markers.     

In transit: Changing social networks of sub-Saharan African migrants in Turkey and Greece

Research on the role of social networks in human migration has mainly relied on single snapshots in time. This paper focuses on the changes in composition and usage of the transnational networks of migrants and why these changes occur. It is based on ethnography and network analysis with forty sub-Saharan African migrants in two transit contexts: Turkey and Greece, over a 17-month period. Findings show that relationship preferences, resources and communication infrastructures constitute an individual opportunity infrastructure affecting how critical events produce network changes. This process is ongoing through the continued experience of critical events, suggesting that the role of networks fluctuates over time.     

Bibliometrics of Big Data Research Literature in Social Science

[Purpose/Significance] This article provides a bibliometric study of the big data research literature in social science to explore its features and patterns both in domestic and international area, quantitatively and qualitatively. [Method/Process] This article collects the data from academic databases, then makes statistical analysis on significant characteristics to evaluate the regularity of document increase, distribution of subject categories, most prolific and impactful institutions, and then discusses the relations between research front and intellectual base via citation and co-citation analysis, finally concludes research features and patterns of big data. [Result/Conclusion] (1) Different subject categories show the different level of research interest while big data research has been one of the most popular topics in social science. The most documents are from information science and library science. (2) Besides journals of high output subject categories, computer science, telecommunications and multidisciplinary journals are common intellectual bases of big data research in social science. (3) Renmin University of China in domestic and Harvard University in the world are top institutions according to the number of fruits and influence of their publications of big data research in social science. (4) For China, publication quantity in social science makes apparent growth while international academic influence is still limited.     

Estimation of Sobol's sensitivity indices under generalized linear models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify through the SSI analysis of a public microarray dataset several novel higher-order gene–gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package Sobol sensitivity.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

Violence in the name of god? A framing processes approach to the Islamic State in Iraq and Syria

Since summer 2014, the insurgent group ‘Islamic State in Iraq and Syria’ (ISIS) has become a major concern for international politics and global security due to its rapid territorial gains, violent operations and the propagation of Salafi-jihadist ideology. This study aims to enhance the academic understanding of ISIS by demystifying the ideological reasoning behind its use of violence. It therefore investigates the link between structural factors that served ISIS’s evolution, its ideological outlook and the significance of this ideology to legitimize violent action. As its theoretical basis, the study employs framing processes within the study of social movements. Methodologically, discursive frame analysis serves to explore the relation of ISIS’s ideology to structural events and experiences to better understand how the group justifies violence. Therefore, the study draws on audio speeches and issues of the magazines Dabiq and Dar al-Islam published by ISIS, which are examined on the rhetoric of othering, collective identity and justifying violence. It is argued that ISIS constructs a collective action frame which creates a social reality that bestows the group with a rationale for action. ISIS’s ideology, based on Islamic symbolism, presents an interpretative lens which assigns meaning to the structural environment of ISIS’s emergence. In this context, violence is justified as a necessity to defend Islam and as an obligation for the true Muslim believer. The discussion concludes that ISIS’s ideology legitimizes the very existence of the group and conceals its mundane struggle for power, territory and wealth through reference to divine authority.