'Beheading', Rule Manipulation and Fraud: The Approval of Election Results in Brazil, 1894-1930

Studies of electoral fraud tend to focus their analyses only on the pre-electoral or electoral phases. By examining the Brazilian First Republic (1889-1930), this article shifts the focus to a later phase, discussing a particular type of electoral fraud that has been little explored by the literature, namely, that perpetrated by the legislatures themselves during the process of giving final approval to election results. The Brazilian case is interesting because of a practice known as degola ('beheading') whereby electoral results were altered when Congress decided on which deputies to certify as duly elected. This has come to be seen as a widespread and standard practice in this period. However, this article shows that this final phase of rubber-stamping or overturning election results was important not because of the number of degolas, which was actually much lower than the literature would have us believe, but chiefly because of their strategic use during moments of political uncertainty. It argues that the congressional certification of electoral results was deployed as a key tool in ensuring the political stability of the Republican regime in the absence of an electoral court.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Guidelines for the Development of Comprehensive Care Centers for Congenital Adrenal Hyperplasia: Guidance from the CARES Foundation Initiative

Patients with rare and complex diseases such as congenital adrenal hyperplasia (CAH) often receive fragmented and inadequate care unless efforts are coordinated among providers. Translating the concepts of the medical home and comprehensive health care for individuals with CAH offers many benefits for the affected individuals and their families. This manuscript represents the recommendations of a 1.5 day meeting held in September 2009 to discuss the ideal goals for comprehensive care centers for newborns, infants, children, adolescents, and adults with CAH. Participants included pediatric endocrinologists, internal medicine and reproductive endocrinologists, pediatric urologists, pediatric surgeons, psychologists, and pediatric endocrine nurse educators. One unique aspect of this meeting was the active participation of individuals personally affected by CAH as patients or parents of patients. Representatives of Health Research and Services Administration (HRSA), New York-Mid-Atlantic Consortium for Genetics and Newborn Screening Services (NYMAC), and National Newborn Screening and Genetics Resource Center (NNSGRC) also participated. Thus, this document should serve as a "roadmap" for the development phases of comprehensive care centers (CCC) for individuals and families affected by CAH.