Styles of Representation in Constituencies in the Homeland and Abroad: The Case of Italy

2020 ◽  
Author(s):  
Eva Østergaard-Nielsen ◽  
Stefano Camatarri
Keyword(s):  
Original Data ◽  
Role Orientation ◽  
International Mobility ◽  
Data Set ◽  
Democratic Representation ◽  
Public Debates ◽  
At Home

Abstract The role orientation of political representatives and candidates is a longstanding concern in studies of democratic representation. The growing trend in countries to allow citizens abroad to candidate in homeland elections from afar provides an interesting opportunity for understanding how international mobility and context influences ideas of representation among these emigrant candidates. In public debates, emigrant candidates are often portrayed as delegates of the emigrant constituencies. However, drawing on the paradigmatic case of Italy and an original data set comprising emigrant candidates, we show that the perceptions of styles of representation abroad are more complex. Systemic differences between electoral districts at home and abroad are relevant for explaining why and how candidates develop a trustee or delegate orientation.

Rotational Characteristics of the Green Solar Corona: 1947-1991

1994 ◽  
Vol 144 ◽  
pp. 139-141 ◽  
Author(s):  
J. Rybák ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Solar Corona ◽  
World Wide ◽  
Rotation Period ◽  
Original Data ◽  
Coronal Emission ◽  
Time Intervals ◽  
Data Set ◽  
The World ◽  
Coronal Emission Line ◽  
Homogeneous Data

AbstractFe XIV 530.3 nm coronal emission line observations have been used for the estimation of the green solar corona rotation. A homogeneous data set, created from measurements of the world-wide coronagraphic network, has been examined with a help of correlation analysis to reveal the averaged synodic rotation period as a function of latitude and time over the epoch from 1947 to 1991.The values of the synodic rotation period obtained for this epoch for the whole range of latitudes and a latitude band ±30° are 27.52±0.12 days and 26.95±0.21 days, resp. A differential rotation of green solar corona, with local period maxima around ±60° and minimum of the rotation period at the equator, was confirmed. No clear cyclic variation of the rotation has been found for examinated epoch but some monotonic trends for some time intervals are presented.A detailed investigation of the original data and their correlation functions has shown that an existence of sufficiently reliable tracers is not evident for the whole set of examinated data. This should be taken into account in future more precise estimations of the green corona rotation period.

Electing the Senate

2017 ◽  
Author(s):  
Wendy J. Schiller ◽  
Charles Stewart III
Keyword(s):  
Original Data ◽  
State Legislators ◽  
Internal Conflict ◽  
Data Set ◽  
Senate Elections ◽  
Political Actors ◽  
The Public ◽  
Seventeenth Amendment ◽  
The People ◽  
Election Process

From 1789 to 1913, U.S. senators were not directly elected by the people—instead the Constitution mandated that they be chosen by state legislators. This radically changed in 1913, when the Seventeenth Amendment to the Constitution was ratified, giving the public a direct vote. This book investigates the electoral connections among constituents, state legislators, political parties, and U.S. senators during the age of indirect elections. The book finds that even though parties controlled the partisan affiliation of the winning candidate for Senate, they had much less control over the universe of candidates who competed for votes in Senate elections and the parties did not always succeed in resolving internal conflict among their rank and file. Party politics, money, and personal ambition dominated the election process, in a system originally designed to insulate the Senate from public pressure. The book uses an original data set of all the roll call votes cast by state legislators for U.S. senators from 1871 to 1913 and all state legislators who served during this time. Newspaper and biographical accounts uncover vivid stories of the political maneuvering, corruption, and partisanship—played out by elite political actors, from elected officials, to party machine bosses, to wealthy business owners—that dominated the indirect Senate elections process. The book raises important questions about the effectiveness of Constitutional reforms, such as the Seventeenth Amendment, that promised to produce a more responsive and accountable government.

Do the Heterogeneous Determinants of Repayment Affect Differently across Borrowers of Diverse Credit Sources in Rural Assam? A Double Hurdle Approach

2021 ◽  
pp. 245513332110316
Author(s):  
Tiken Das ◽  
Pradyut Guha ◽  
Diganta Das
Keyword(s):  
Rural Areas ◽  
Probit Model ◽  
Original Data ◽  
Community Based ◽  
Data Set ◽  
Brahmaputra Valley ◽  
Double Hurdle ◽  
Instrumental Variable Probit ◽  
Instrumental Variable Probit Model

This study made an attempt to answer the question: Do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently? The study is based on data collected from 240 households from three districts in the lower Brahmaputra valley of Assam through a carefully designed primary survey. Besides, the study uses the double hurdle approach and the instrumental variable probit model to reduce possible selection bias. It observes better repayment performance among formal borrowers, followed by semiformal borrowers, while occupation wise it is prominent among organised employees. It has been found that in general, the household characteristics, loan characteristics and location-specific characteristics significantly affect repayment performance of borrowers. However, the nature of impact of the factors influencing repayment performance is remarkably different across credit sources. It ignores the role of traditional community-based organisations in rural Assam while analysing the determinants of repayment performance. The study also recommends for ensuring productive opportunities and efficient market linkages in rural areas of Assam. The study is based on an original data set that has specially been collected to examine question that—do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently in the lower Brahmaputra valley of Assam—which has not been studied before.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

scholarly journals Vielfalt and diversité: how local actors in France and Germany evaluate immigration and socio-cultural heterogeneity

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Maria Schiller ◽  
Christine Lang ◽  
Karen Schönwälder ◽  
Michalis Moutselos
Keyword(s):  
Local Politics ◽  
Original Data ◽  
National Politics ◽  
Data Set ◽  
Large Cities ◽  
Cultural Heterogeneity ◽  
Societal Actors ◽  
Local Actors ◽  
Urban Level ◽  
And Migration

AbstractIn both Germany and France, perceptions of immigration, diversity and their societal consequences have undergone important transformations in the past two decades. However, existing research has only partially captured such processes. The “grand narratives” of national approaches, while still influential, no longer explain contemporary realities. Further, analyses of national politics and discourses may not sufficiently reflect the realities across localities and society more broadly. While emerging in different national contexts, little is known about how diversity is actually perceived by political stakeholders at the urban level. Given the key role of immigration and diversity in current conflicts over Europe’s future, it is imperative to assess present-day conceptualisations of migration-related diversity among important societal actors.This article investigates perceptions and evaluations of socio-cultural heterogeneity by important societal actors in large cities. We contribute to existing literature by capturing an unusually broad set of actors from state and civil society. We also present data drawn from an unusually large number of cities. How influential is the perception of current society as heterogeneous, and what forms of heterogeneity are salient? And is socio-cultural and migration-related heterogeneity evaluated as threatening or rather as beneficial? Based on an original data set, this study explores the shared and contested ideas, the cognitive roadmaps of state and non-state actors involved in local politics.We argue that, in both German and French cities, socio-cultural heterogeneity is nowadays widely recognized as marking cities and often positively connoted. At the same time, perceptions of the main features of diversity and of the benefits and challenges attached to it vary. We find commonalities between French and German local actors, but also clear differences. In concluding, we suggest how and why national contexts importantly shape evaluations of diversity.

How local personal vote-earning attributes affect the aggregate party vote share: Evidence from the Belgian flexible-list PR system (2003–2014)

Politics ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 464-479
Author(s):  
Gert-Jan Put ◽  
Jef Smulders ◽  
Bart Maddens
Keyword(s):  
Regression Models ◽  
Empirical Test ◽  
Original Data ◽  
Ordinary Least Squares ◽  
Vote Share ◽  
District Level ◽  
National Party ◽  
Data Set ◽  
Geographically Distributed ◽  
Party Vote Shares

This article investigates the effect of candidates exhibiting local personal vote-earning attributes (PVEA) on the aggregate party vote share at the district level. Previous research has often assumed that packing ballot lists with localized candidates increases the aggregate party vote and seat shares. We present a strict empirical test of this argument by analysing the relative electoral swing of ballot lists at the district level, a measure of change in party vote shares which controls for the national party trend and previous party results in the district. The analysis is based on data of 7527 candidacies during six Belgian regional and federal election cycles between 2003 and 2014, which is aggregated to an original data set of 223 ballot lists. The ordinary least squares (OLS) regression models do not show a significant effect of candidates exhibiting local PVEA on relative electoral swing of ballot lists. However, the results suggest that ballot lists do benefit electorally if candidates with local PVEA are geographically distributed over different municipalities in the district.

Drug Use During Pregnancy Policies in the United States From 1970 to 2016

2018 ◽  
Vol 45 (4) ◽  
pp. 441-459 ◽  
Author(s):  
Sue Thomas ◽  
Ryan Treffers ◽  
Nancy F. Berglas ◽  
Laurie Drabble ◽  
Sarah C. M. Roberts
Keyword(s):  
Drug Use ◽  
State Level ◽  
Original Data ◽  
The United States ◽  
State Policies ◽  
Alcohol Policy ◽  
Data Set ◽  
Drug Laws ◽  
Policy Domain ◽  
Policy Environments

As U.S. states legalize marijuana and as governmental attention is paid to the “opioid crisis,” state policies pertaining to drug use during pregnancy are increasingly important. Little is known about the scope of state policies targeting drug use during pregnancy, how they have evolved, and how they compare to alcohol use during pregnancy policies. Method: Our 46-year original data set of statutes and regulations in U.S. states covers the entirety of state-level legislation in this policy domain. Data were obtained through original legal research and from the National Institute on Alcohol Abuse and Alcoholism’s Alcohol Policy Information System. Policies were analyzed individually as well as by classification as punitive toward or supportive of women. Results: The number of states with drug use during pregnancy policies has increased from 1 in 1974 to 43 in 2016. Policies started as punitive. By the mid- to late 1980s, supportive policies emerged, and mixed policy environments dominated in the 2000s. Overall, drug/pregnancy policy environments have become less supportive over time. Comparisons of drug laws to alcohol laws show that the policy trajectories started in opposite directions, but by 2016, the results were the same: Punitive policies were more prevalent than supportive policies across states. Moreover, there is a great deal of overlap between drug use during pregnancy policies and alcohol/pregnancy policies. Conclusion: This study breaks new ground. More studies are needed that explore the effects of these policies on alcohol and other drug use by pregnant women and on birth outcomes.

scholarly journals The Corporate War Dead: New Perspectives on the Demographics of American and British Contractors

2018 ◽  
Vol 46 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Ori Swed ◽  
Jae Kwon ◽  
Bryan Feldscher ◽  
Thomas Crosbie
Keyword(s):  
Lived Experience ◽  
Original Data ◽  
Military Operations ◽  
Data Set ◽  
Military Organizations ◽  
Security Industry ◽  
Legal Implications ◽  
History Of ◽  
War Dead ◽  
Military Activity

From an obscure sector synonymous with mercenaryism, the private military and security industry has grown to become a significant complementing instrument in military operations. This rise has brought with it a considerable attention. Researchers have examined the role of private military and security companies in international relations as well as the history of these companies, and, above all, the legal implications of their use in the place of military organizations. As research progresses, a significant gap has become clear. Only a handful of studies have addressed the complex of issues associated with contractors’ demographics and lived experience. This article sheds some light over this lacuna, examining contractors’ demographics using descriptive statistics from an original data set of American and British contractors who died in Iraq between the years 2003 and 2016. The article augments our understanding of an important population of post-Fordist-contracted workforce, those peripheral workers supplementing military activity in high-risk occupations with uncertain long-term outcomes.

scholarly journals Embedding Undersampling Rotation Forest for Imbalanced Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Huaping Guo ◽  
Xiaoyu Diao ◽  
Hongbing Liu
Keyword(s):  
Imbalanced Data ◽  
Feature Space ◽  
Original Data ◽  
Training Set ◽  
Data Set ◽  
Minority Class ◽  
Rotation Forest ◽  
Novel Method ◽  
Individual Classifier ◽  
The Cost

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

Bayesian Classifier for Sparsity-Promoting Feature Selection

2015 ◽  
Vol 29 (06) ◽  
pp. 1550022 ◽  
Author(s):  
Danlei Xu ◽  
Lan Du ◽  
Hongwei Liu ◽  
Penghui Wang
Keyword(s):  
Feature Selection ◽  
Synthetic Data ◽  
Original Data ◽  
Radar Data ◽  
Bayesian Classifier ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Classification Boundary ◽  
Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

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