Why Do Immigrants and Refugees Give Back to Their Communities and What can We Learn from Their Civic Engagement?

Each year thousands of immigrants and refugees begin their lives in new places, speaking new languages, and facing new challenges. Challenges include access to health/mental care, education, transportation, and employment. Researchers and practitioners frequently focus on challenges of newcomers and their deficits in meeting needs for self-sufficiency. This study explores newcomers’ giving back and emphasizes an untapped reservoir of strength and capacity. Based on qualitative semi-structured interviews with 54 immigrants and refugees, themes identified include (1) a desire to maintain ethnic identity and connection; (2) ethnic community as an extension of family; (3) a sense of duty and obligation; and (4) measure of achieved success. Researchers and practitioners should shift their view to recognize the strengths and capacities of newcomers who give back to their communities.     

Semiparametric partially linear varying coefficient models with panel count data

This paper studies semiparametric regression analysis of panel count data, which arise naturally when recurrent events are considered. Such data frequently occur in medical follow-up studies and reliability experiments, for example. To explore the nonlinear interactions between covariates, we propose a class of partially linear models with possibly varying coefficients for the mean function of the counting processes with panel count data. The functional coefficients are estimated by B-spline function approximations. The estimation procedures are based on maximum pseudo-likelihood and likelihood approaches and they are easy to implement. The asymptotic properties of the resulting estimators are established, and their finite-sample performance is assessed by Monte Carlo simulation studies. We also demonstrate the value of the proposed method by the analysis of a cancer data set, where the new modeling approach provides more comprehensive information than the usual proportional mean model.     

Post-disaster fertility: Hurricane Katrina and the changing racial composition of New Orleans

Large-scale climate events can have enduring effects on population size and composition. Natural disasters affect population fertility through multiple mechanisms, including displacement, demand for children, and reproductive care access. Fertility effects, in turn, influence the size and composition of new birth cohorts, extending the reach of climate events across generations. We study these processes in New Orleans during the decade spanning Hurricane Katrina. We combine census data, ACS data, and vital statistics data to describe fertility in New Orleans and seven comparison cities. Following Katrina, displacement contributed to a 30% decline in birth cohort size. Black fertility fell, and remained 4% below expected values through 2010. By contrast, white fertility increased by 5%. The largest share of births now occurs to white women. These fertility differences—beyond migration-driven population change—generate additional pressure on the renewal of New Orleans as a city in which the black population is substantially smaller in the disaster’s wake.     

Civil Society Organisations in Research: A Literature-Based Typology

This article explores literatures from various sources to highlight and understand differences among key players surrounding the perceived nature and role of civil society in research from different literature streams. Including Civil Society Organisations (CSOs) in research activities is an integral part of a broad drive towards integration of science and society. Interest in CSO inclusion in research is widespread, but lacks a coherent focus and clarity on what CSOs are. Without this clarity, CSO-inclusive research, or policy, may be ineffective. This article addresses this gap in knowledge by presenting findings from an exploration of academic, policy and research project literature in order to come to a view on CSOs in research. This culminates in a typology of CSOs and provides a means of identifying types of CSOs. The typology shows four main types of CSO (common cause, shared voice, research-oriented, commercially oriented) and provides a definition for each type, along with a basis for the definition; an example of each; some typical terminology; typical area of activity; properties; typical mission; key areas of interest and their ‘action logic’ in research.     

Comparison of performance measures for multivariate discrete models

For multivariate probit models, Spiess and Tutz suggest three alternative performance measures, which are all based on the decomposition of the variation. The multivariate probit model can be seen as a special case of the discrete copula model. This paper proposes some new measures based on the value of the likelihood function and the prediction-realization table. In addition, it generalizes the measures from Spiess and Tutz for the discrete copula model. Results of a simulation study designed to compare the different measures in various situations are presented.     

Accuracy of approximate progressively censored reliability sampling plans for exponential models

Reliability sampling plans provide an efficient method to determine the acceptability of a product based upon the lifelengths of some test units. Usually, they depend on the producer and consumer’s quality requirements and do not admit closed form solutions. Acceptance sampling plans for one- and two-parameter exponential lifetime models, derived by approximating the operating characteristic curve, are presented in this paper. The accuracy of these approximate plans, which are explicitly expressible and valid for failure and progressive censoring, is assessed. The approximation proposed in the one-parameter case is found to be practically exact. Explicit lower and upper bounds on the smallest sample size are given in the two-parameter case. Some additional advantages are also pointed out.