Myriad potential for mentoring: Understanding the process of transformational change through a gender equality intervention

This article contributes to understanding transformational change towards gender equality by examining the transformational change potential of a mentoring programme for women, a type of gender equality intervention both criticized and praised for its ability to bring about change. Drawing upon an empirical case study of a mentoring programme for women academics in a Dutch university, we explore three dimensions of transformational change: organizational members (i) discussing and reflecting upon gendered organizational norms and work practices; (ii) creating new narratives; and (iii) experimenting with new work practices. Our findings indicate five specific conditions that enable transformational change: cross‐mentoring, questioning what is taken for granted, repeating participation and individual stories, facilitating peer support networks and addressing and equipping all participants as change agents. We suggest that these conditions should be taken into account when (re)designing effective organizational gender equality interventions.     

Seeing the system: Systemic gender knowledge to support transformational change towards gender equality in science

Scholars agree that gender inequality is systemic and that participants in gender equality interventions need knowledge on gender inequality processes. However, a detailed view on the specific characteristics of this knowledge is as yet missing. This article aims to contribute to gender equality interventions by conceptualizing and visualizing systemic gender knowledge as an important condition for transformational change. Combining gender and participatory system dynamics literature, this article first introduces the concept of systemic gender knowledge. This concept captures two main characteristics that make gender knowledge systemic: knowledge on the interaction of gender inequality processes and endogenous thinking, here implying a focus on the organization as the relevant level of analysis. In addition to this conceptual contribution, the research contributes methodologically to the gender inequality intervention literature by designing a visualization process, translating written texts into system dynamics models which enable exploration of systemic gender knowledge. Finally, the research contributes empirically by exploring the systemic gender knowledge of participants in two science research institutes of a Dutch university, finding shifts in both characteristics of systemic gender knowledge. This enables researchers to discern whether gender equality interventions lead to increases in systemic gender knowledge, thus supporting transformational change.     

Children's Perspectives on Happiness and Subjective Well‐being in Preschool

Photo‐elicited interviews indicate that children hardly ever mention educators when asked about elements in preschool that make them feel happy. Happiness is found to occur in activities in the ‘underlife’ of the ECEC institution. Children challenge adult rules and norms in order to create status in the peer‐group, while at the same time, they seek to construct social identity and maintain a positive relationship with their educators. A child that manages to balance both adult expectations and what is needed to participate in the underlife among peers, experiences happiness and thus, is in a good state of well‐being.     

The Politics and Discourse of Migrant Return: The Role of UNHCR and IOM in the Governance of Return

This article proposes that there is added value in moving beyond isolated studies of return-related migration policies in order to consider both deportations and so-called assisted voluntary returns under the common heading of ‘state-induced returns’. Based on official documents and interviews with staff members of the United Nations High Commissioner for Refugees and the International Organisation for Migration, it argues that international actors working in the field of migrant return engage in a type of task-sharing that goes beyond functional complementariness. With regard to the return of rejected asylum seekers, for instance, they legitimise each other's engagement as well as the overarching return objectives of governments, and are, therefore, involved in norm-building regarding the acceptability of state-induced returns. In addition to setting certain minimum standards regarding states' treatment of their immigrant population, international actors assist states in upholding control over them. Rather than merely replacing state-led regulation, international actors thus support domestic governments in reaching their migration control objectives, and thereby contribute to a stabilisation of state sovereignty in the governance of migration.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.     

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols

In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the compliance with the EMA guideline, we evaluated the model specifications in those clinical study protocols from development phases II and III submitted between 2015 and 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy. Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modeling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%), and the fallback strategy (18%) are given by less than half the study protocols. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type I error rate control at the intended level is not guaranteed.     

Beyond Contact? Policy approaches to work with families of looked after children in four European countries

In England, placement within the looked after system is not viewed as a desirable long‐term solution for most children, and policy has prioritised continued contact with parents, and swift return home, wherever possible. This review examines policy approaches to work with families of looked after children in England and in three other European countries: Denmark, France and the Netherlands, aiming to identify areas for shared learning in relation to this challenging area of policy and practice. The research highlights relationships between care populations and policy understandings of the purpose of work with families, including understandings of children's and/or parents’ rights.     

Digital Finance and FinTech: current research and future research directions

Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction—delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.