Social Accountability in Diaspora Organizations Aiding Syrian Migrants

This article presents data from a study of Syrian diaspora organizations providing assistance to conflict‐affected Syrians in Europe, the Middle East, and North America. Using interview data from leaders in four Syrian diaspora non‐profit organizations, this article examines the social mechanisms used to ensure accountability within the challenging environments where the organizations operate. We find that Syrian diaspora organizations benefit from informal social accountability mechanisms that derive from individuals’ social network ties. Personal, social forms of accountability are particularly valuable to these organizations because these mechanisms help circumvent uncertainty and challenges in the operational environment. These findings reflect an important theme in the extant literature on diaspora philanthropy: diaspora members may have an advantage over other actors because of dense personal networks that make them adept at identifying dependable partners and enforcing agreements even in places where banking and legal systems are fragile.     

Creating a Favourable Deployment Mechanism of Oil and Gas Revenues with Regard to Volatile Oil Prices: The Case of Azerbaijan

This paper studies optimal mechanisms of intergenerational fair distribution of oil and gas revenues in terms of oil price shocks in the case of Azerbaijan. In order to determine optimal utilization mechanism of oil and gas revenues the following models: real constant expenditure (permanent income hypothesis), nominal constant expenditure, stable diminishing expenditure and stable rate diminishing expenditure, were tested on the basis of three alternative oil and gas export prices. Finally the research suggests the statistical method to calculate crude oil reference price for the midterm fiscal planning and the flexible mechanism which can be revised with regard to oil price uncertainty.     

Multiculturalism and political integration in Birmingham and Bradford

The article discusses multiculturalism and political integration in Birmingham and Bradford, two cities that are amongst the main urban areas of immigrant settlement in England outside London. The article focuses especially on the subject of ethnic mobilization, and describes for each ethnic community in each of the two cities the type and character of their organizations and their role in multi‐culturalist politics. On the basis of the evidence presented for each city it is concluded that despite some negative indications about the success of multiculturalism, there are also very many positive signs which speak in favour of a limited claim for multiculturalism, as delineating the creation of a political situation in which relatively new ethnic minorities are able to have the protection of a cultural home and the resource of ethnic culture to provide solidarity as they fight for their rights. This limited claim for multiculturalism is far from being incompatible with democracy. It may even serve to strengthen it     

ESTIMATION OF SCALE AND LOCATION PARAMETERS, USING QUADRATIC COEFFICIENTS

A particular linear estimate with three terms approximation using ordered observations is discussed which seems to be simple to calculate. This estimate is applicable for any sample size for some special distributions without considerable loss in efficiency and is, therefore, of practical value.     

Diverse classifier ensemble creation based on heuristic dataset modification

Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art.