Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

“Migrant Capital” and Domestic Work: Labour Trajectories of Immigrant Women in Spain

This article analyses the labour trajectory of migrant women in domestic service. The research considers women's working conditions upon arrival, or their “migrant capital” (i.e. their human, social and economic capital) as the defining factors in their labour trajectories. The study, conducted on a sample of migrant women in domestic service, reveals the different value each type of capital has at each stage of a labour trajectory. The social network is the core capital in their first job. Nevertheless, the key factors in labour mobility are human capital and a household's financial needs. The processes of administrative regularization and family reunification prompt far‐reaching changes in these women's labour trajectories. Finally, labour trajectories during the economic crisis have been shaped by financial needs, leading to a decapitalization of the human capital acquired, with even legal status surrendering its value.     

Urban ecosystem Services in Latin America: mismatch between global concepts and regional realities?

Urban Ecosystems - Latin America and the Caribbean (LAC) is one of the most urbanized and biologically diverse regions in the world but is often characterized by weak environmental governance and...     

Finding common ground: how superdiversity is unsettling social work education

For social work education in the Netherlands, the rapid transition towards superdiversity means that its capacity to adapt to this new reality and to adjust to diverse students’ backgrounds is fiercely challenged. The key aim of this article is to discuss how social work educators are dealing with the unsettling challenges of increasing diversity, based on the research outcomes of an explorative study amongst Turkish–Dutch and Moroccan–Dutch female professionals. Two different groups of educators were asked to comment on the outcomes of this study. One of the dilemmas for educators is to determine what can be considered supportive and inclusive and as reducing inequality in education. The authors used two key theoretical concepts, ‘superdiversity’ and ‘the capability approach’. These theoretical perspectives were used to deconstruct the rather massive concepts of diversity and social justice, by emphasising contextual approaches. Both perspectives stress the urgent need to involve students as ‘active agents of change’, by building a social work community to stimulate and facilitate an on-going dialogue. To successfully fulfil a pivotal role in the upward mobility or emancipation of ethnic minority students, social work education needs to be adjusted to accommodate diverse student groups.     

‘My profession is gone’: how social workers experience de-professionalization in the Netherlands

Recently social work in the Netherlands underwent two major changes. Specialized agencies were replaced by one-stop shops (district teams) and the welfare state was replaced by a ‘participation society’, in which vulnerable groups have to rely on their social network rather than resort to professional care. The first change is termed ‘de-specialization’; the second ‘basic de-professionalization’. The research question in this article is: how do Dutch social workers experience and evaluate these two developments? Qualitative interviews with 29 experienced social workers show that most of them endorse de-specialization, as this type of aid is deemed better for clients with complex problems. Moreover, many social workers like to take on new tasks. Basic de-professionalization is met with more reserve. Social workers observe that many clients do not have a suitable network and need professional help. In addition, they feel that their profession is being degraded as lay people and volunteers take over (part of) their work. However, they feel unable to resist this development, because resistance might get them sacked. Some social workers even enhance basic de-professionalization because of their willingness to continue working as (retired) volunteers. Social workers and theorists need to address and counter the move towards basic de-professionalization.     

Changing Indigeneity Politics in Indonesia: From Revival to Projects

Through the discourse of indigeneity, rural communities around the world are joining a global network of rural justice seekers. By articulating grievances collectively, they demand state recognition while seeking support from NGOs and international development organisations. In Indonesia, the manifestation of indigenous ‘adat’ politics is no longer confined to the national struggle for the recognition of land rights, but instead, has proliferated into many localised short term ‘adat projects’. This introduction to the TAPJA special issue on adat demonstrates that both the rural poor and local elites can be the initiators or recipients of these adat projects but, at the current juncture, the latter are better positioned to benefit from such projects. The special issue shows that in Indonesia, where adat is often firmly entrenched in the state, the promotion of indigeneity claims can work in contradictory ways. Findings from across the special issue show that adat projects tend to reinforce the power of the state, rather than challenging it.     

Early-life mortality clustering in families: A literature review

Research on early-life mortality in contemporary and historical populations has shown that infant and child mortality tend to cluster in a limited number of high-mortality families, a phenomenon known as ‘mortality clustering’. This paper is the first to review the literature on the role of the family in early-life mortality. Contemporary results, methodological and theoretical shortfalls, recent developments, and opportunities for future research are all discussed in this review. Four methodological approaches are distinguished: those based on sibling deaths, mother heterogeneity, thresholds, and excess deaths in populations. It has become clear from research to date that the death of an older child harms the survival chances of younger children in that family, and that fertility behaviour, earlier stillbirths, remarriages, and socio-economic status all explain mortality clustering to some extent.