A Combined Formal Model for Relational Context-Dependent Roles (Extended)

Role-based modeling has been investigated for over 35 years as a promising paradigm to model complex, dynamic systems. Although current software systems are characterized by increasing complexity and context-dependence, all this research had almost no influence on current software development practice, still being discussed in recent literature. One reason for this is the lack of a coherent, comprehensive, readily applicable notion of roles. Researchers focused either on relational roles or context-dependent roles rather then combining both natures. Currently, there is no role-based modeling language sufficiently incorporating both the relational and context-dependent nature of roles together with the various proposed constraints. Hence, this paper formalizes a full-fledged role-based modeling language supporting both natures. To show its sufficiency and adequacy, a real world example is employed

Alternative approaches for studying shared and distributed leadership

Scholars hold different perspectives about leadership which are not limited to a formally appointed leader. Of the abundance of terms used to describe this phenomenon, shared and distributed are the most prevalent. These terms are often used interchangeably, resulting in confusion in the way that shared and distributed leadership is conceptualized and investigated. This paper provides a historical development of this field, challenges existing conceptions and reveals inconsistencies and contradictions that are seldom acknowledged. Four distinct approaches to the study of shared and distributed leadership are identified in the literature, each embracing different ontological views and leadership epistemologies. Individually, the four approaches offer valuable - yet partial - understanding. Comparing and contrasting the assumptions and insights from the four approaches raises fundamental issues about how we think about leadership in terms of research, practice and development

The Antecedents of a ‘Chilly Climate’ for Women Faculty in Higher Education

The literature on women’s under-representation in academia asserts that faculty women face a ‘chilly climate’, but there are few theoretically based studies examining this proposition. Relational demography, organizational justice, and social network theories all identify possible antecedents of ‘chilly climate’. Using survey data of faculty at a private Midwestern US university, we test whether the perception of exclusion (chilly climate) is influenced by demographic dissimilarity, and perceptions of fairness and gender equity. We find that faculty women perceive more exclusion from academic departments with a low representation of women, consistent with relational demography. Perceptions of procedural fairness and gender equity are powerful factors that foster inclusion and warm the climate for both men and women. The ‘chilly climate’ for women faculty is a complex phenomenon with multiple causes. Policies that fail to address these multiple causes are unlikely to be effective

Roles played by relational trust in strategic alliances

In this paper we develop the concept of relational quality as a proxy for relational trust. Exploration of data from 67 alliances confirms that relational quality is composed of three elements: initial conditions, partner interactions, and external events. We offer propositions on the relative importance of each of these elements depending on the different roles relational trust may play in strategic alliances: the role of a control mechanism, of a governance mechanism, or as an enabler of high risk initiativesManagement; Strategy

Spaces of Innovation: learning, proximity and the ecological turn

Contrary to the fashionable “death of distance” thesis, the socio-spatial context for innovation remains as important as ever for firms, networks and the public institutions that tend to be neglected in orthodox narratives of learning. In this article we explore the changing socio-spatial dynamics of innovation through the medium of three arguments: (i) that the “learning region” debate was worth having because it triggered a fruitful dialogue between innovation theorists and economic geographers; (ii) that geographical proximity remains central to our understanding of learning and innovation and should not be reduced to, or conflated with, physical co-location; and (iii) that “the ecological turn” challenges conventional conceptions of learning, innovation and development, posing unsettling questions about the forces of path dependency, especially in less favoured regions.Learning, regions, innovation, proximities

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

The social patterning of values and rationalities: mothers' choices in combining caring and employment

The assumption of individualised rationality runs through the two dominant theorisations of family behaviour – new household economics and individualisation. We demonstrate the inaccuracy of this assumption, using the results of two CAVA projects into mothers' perceptions and choices in combining mothering with paid work, in allocating tasks with partners, and in choosing childcare. Rather, mothers make such decisions within socially negotiated accounts of what is morally adequate, and we go on to show how these decisions and the values informing them are socially patterned by class and ethnicity. Finally, we consider how both theory and policy can make a ‘rationality mistake’ in neglecting the importance of social ties and moral responsibilities in family life

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes