Managing ‘American Islam’

Focusing on the case of Islam in post-9/11 United States, this article highlights the particularity of how US secularism is enacted by the state. In much sociological theory, the United States has been understood to be a neutral and non-interfering state with regard to religion, thereby fostering a pluralist religious context of free consumer choice. Some Muslim reformists have argued that this context makes the US highly fertile ground upon which to reform Islam and to improve women's status in Islam. This article argues that, in the context of the US-led ‘war on terror’, the government has drawn on and amplified this discourse in the service of producing a representation of the US as tolerant, while also seeking to promote a concept of ‘true’ Islam and produce patriotic Muslim citizens. At the center of this discourse are contested portrayals of Muslim women as symbolic of a modern and liberated Islam that is uniquely ‘American’ and opposed to other presumably oppressive Islams. While this context may in fact promote the reform of Islam, it does so with state involvement rather than as a result of state neutrality.     

Assessment of hospital services and patient reactions

This paper deals with the study of assessment of hospital services and patient reactions carried out amongst 473 respondents, both in-door (3 wards) and out-door patients in M.L.B. Medical College Hospital, Jhansi (Uttar Pradesh). The patients were from three wards medical, surgical and gynaecological wards and consisted of 373 discharged and 100 out-door patients.     

Bayesian hierarchical model for protein identifications

In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a ‘two-step’ procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins is neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove the assumption of independence of proteins by using clustering group priors to the proteins based on the assumption that proteins sharing the same biological pathway are likely to be present or absent together and are correlated. The complete conditionals of the proposed joint model being tractable, we propose and implement a Gibbs sampling scheme for full posterior inference that provides the estimation and statistical uncertainties of all relevant parameters. The model has better operational characteristics compared to two existing ‘one-step’ procedures on a range of simulation settings as well as on two well-studied datasets.     

The Formation of Transnational Identity among French Immigrants Employed in French‐Speaking Companies in Israel

This study focuses on the formation of a transnational identity among immigrants from France who are employed in French‐speaking companies in Israel (mostly call‐centres). The preliminary qualitative analysis shows that this unique employment pattern contributes to the formation of their transnational identity, which is a combination of their francophone, Jewish and Israeli identity. The findings obtained from a larger‐scale online survey indicated that French immigrants employed in French‐speaking companies are more ethnically, socially and culturally segregated, and less fluent in Hebrew than French immigrants who are not employed in such companies. However, no significant differences were found between these two groups in their Israeli identity and sense of belonging to Israeli society. In general, the French immigrants feel at home in Israel, are satisfied with their life in Israel and plan to remain there. The implications of these findings for policymakers are discussed.     

Combining monotonicity and strong equity: construction and representation of orders on infinite utility streams

This paper studies the nature of social welfare orders (SWO) on infinite utility streams, satisfying the efficiency principle known as monotonicity and the consequentialist equity principle known as strong equity. It provides a complete characterization of domain sets for which there exists such a SWO which is in addition representable by a real valued function. It then shows that for those domain sets for which there is no such SWO which is representable, the existence of such a SWO necessarily entails the existence of a non-Ramsey set, a non-constructive object.     

An experimental test of prospect theory for predicting choice under ambiguity

Prospect theory is the most popular theory for predicting decisions under risk. This paper investigates its predictive power for decisions under ambiguity, using its specification through the source method. We find that it outperforms its most popular alternatives, including subjective expected utility, Choquet expected utility, and three multiple priors theories: maxmin expected utility, maxmax expected utility, and a-maxmin expected utility.