Showing papers on "Specialization (logic) published in 2011"

Knowledge Transformation in Project Networks: A Speech Act Level Cross-Boundary Analysis:

Abstract: Researchers stress the importance of understanding knowledge transformation in projects. To explore how knowledge is transformed across organizational and specialization boundaries in project netwo...

Innovation pathways and policy challenges at the regional level: smart specialization

Abstract: This paper highlights the great diversity in development pathways and trajectories of innovation across European regions. A regional knowledge-based economy has multi-dimensional aspects. It includes a variety of knowledge activities and multiple interactions among a range of actors including universities, research institutes, enterprises, knowledge workers and institutions. The spatial patterns and trends for the different aspects of the knowledge-based economy vary significantly across Europe. Most aspects show convergence and generate catching-up processes, while some show divergence between European regions. Overall, absorption capacity has increased in importance and education is an important challenge for future regional development. Place-based innovation policy is essential to enhance synergies among co-evolving knowledge capabilities and encourage smart specialisation.

Sociosystemics, statistics, decisions

Abstract: The current condition of knowledge about social world as reflected in political sciences, economics, management sciences, sociology, psychology, and their many derivatives is in deep disarray. It can be observed in the poor representation of the obtained results of scientific findings in practical issues as well as in the internal problems and inconsistencies within the respective branches of science. The exponentially growing volume of information in these areas is supplemented with less than linear growth of real knowledge, and even this one cannot be considered “hard knowledge” for many reasons. This phenomenon is not new, but the alarming gap between two flows of symbolic realm on the one hand, and between these flows as a whole and real practices on the other hand, is growing faster with every year. Further scientific specialization seems just to widen this gap. A need for some unifying principles overcoming narrow boundaries of particular sciences and even their composites (like behavioral economics or social psychology) becomes more and more clear. The attempts to solve universal problems using such grand concepts as general theory of systems, cybernetics, sociophysics, statistics have yielded many brilliant results, but haven’t proven to become the sought-after unifying frame in many aspects. One of the reasons for that was their orientation to specific types of models, which, applied to social reality, do not work as expected. In this article, I’ll try to introduce some principles of a new science, sociosystemics, which will hopefully help to transform over the cross a time the enormous volume of social information into meaningful knowledge to be used for analysis, prediction, finding innovative solutions, and decision making.

Designing a model for evaluation of knowledge management level in industrial organizations of Iran (Auto industry)

Abstract: In today's world, knowledge is the only way of gaining wealth in organizations and societies. Natural and human investments may result in wealth only when coordinated and incorporated with knowledge investments. Knowledge, information, spiritual assets, specialization, and occupational qualifications are the required instruments for gaining wealth, and those societies deprived of such instruments are regarded as poor. Nowadays, capability relies heavily on knowledge, and it is the application of creative mentality that effectively makes promotion and development possible. Organizations have realized that no investment benefits them in today's world of competition as much as knowledge does. That is why above all other factors, the personnel of an organization (being the source of knowledge) have been considered as the most critical investments of that organization. Accordingly, knowledge management, as the instrument by which the available knowledge can be collected, classified, and disseminated throughout the organization, has received special attention. However, the efforts of many organizations in the field of knowledge management have ended in failure. This has been mostly due to the fact that these organizations have not taken advantage of the proper model of knowledge management evaluation, and they have looked upon it as a temporary and short term task. In this article, in accordance to the research done in this regard, the effective factors involved in knowledge management have been determined, and based on these factors; a model of knowledge management evaluation in industrial organizations (automobile industry) of Iran has been presented. In addition to that, using the presented model, we can assess the level of knowledge management. These factors are recognizing knowledge, acquiring knowledge, application of knowledge, sharing of knowledge, expansion of knowledge, and maintenance of knowledge. Each of these factors has been subdivided into other factors as well. Two organizations of the automobile industry of Iran, which have had the highest level of production in the automobile market of Iran among all other organizations, have been studied in this research. After collecting the data, applying the necessary statistical procedures, and analyzing the outcome, the results have been presented in the form of a final model so that it will be tested on its effectiveness in practice. Key words: Knowledge, knowledge management, knowledge evaluation, level of knowledge, automobile industry.

Introduction to Sociosystemics: Science About the Utilizing of Social Sciences

Abstract: The current condition of knowledge about social world as reflected in political sciences, economics, management sciences, sociology, psychology, and their many derivatives is in deep disarray. It can be observed in the poor representation of the obtained results of scientific findings in practical issues as well as in the internal problems and inconsistencies within the respective branches of science. The exponentially growing volume of information in these areas is supplemented with less than linear growth of real knowledge, and even this one cannot be considered “hard knowledge” for many reasons. This phenomenon is not new, but the alarming gap between two flows of symbolic realm on the one hand, and between these flows as a whole and real practices on the other hand, is growing faster with every year. Further scientific specialization seems just to widen this gap. A need in some unifying principles overcoming narrow boundaries of particular sciences and even their composites (like behavioral economics or social psychology) becomes more and more clear. The attempts to solve universal problems using such grand concepts as general theory of systems, cybernetics, sociophysics, statistics have yielded many brilliant results, but haven’t proven to become the sought-after unifying frame in many aspects. One of the reasons for that was their orientation to specific types of models, which, applied to social reality, do not work as expected. In this article, I’ll try to introduce some principles of a new science, sociosystemics, which will hopefully help to transform the enormous volume of social information into meaningful knowledge to be used for analysis, prediction, finding innovative solutions, and decision making.

Thoughts about Structuralization, Specialization, Instantiation, and Metaization.

Abstract: In knowledge engineering, ontology creation, and especially in knowledge-based configuration often used relations are: aggregate relations (has-parts, here called structural relations), specialization relation (is-a), and instantiation (instance-of). A combination of the later is called metaization, which denotes the use of multiple instantiation layers. Structural and specialization relations are mainly used for organizing the knowledge represented on one layer. Instantiation layers model different kind of knowledge, i.e. knowledge about sets, individuals, and knowledge about knowledge (metaknowledge). By applying reasoning techniques on each layer, reasoning on metaknowledge is enabled.

When to use what: Structuralization, Specialization, Instantiation, Metaization - Some Hints for Knowledge Engineers.

Abstract: In knowledge engineering, ontology creation, and especially in knowledge-based configuration often used relations are: aggregate relations (has-parts, here called structural relations), specialization relation (is-a), and instantiation (instance-of). A combination of the later is called metaization, which denotes the use of multiple instantiation layers. In this paper, we give examples and use-hints for these relations especially from the configuration point of view.

Deterministic-Rule Programs on Specialization Systems: Clause-Model Semantics

Abstract: Deterministic-rule programs provide a bridge between rule-based nondeterministic programs and deterministic programs in imperative languages. In this paper, we formulate deterministic-rule programs on a specialization system and formalize their procedural semantics, called the clause-model semantics, based on a general framework for discussing the semantics of procedures with a conditional state-transition structure. The proposed theory establishes a general class of deterministic-rule programs with their precise semantics, providing a basis for developing methods for synthesis of deterministic programs from declarative descriptions. Taking a specialization system as a parameter, the theory is applicable to many concrete classes of deterministic-rule programs with various forms of data structures through parameter instantiation.

A Social Realist Investigation of Academic Nursing: Specialisation and Semantics

Abstract: Aim: To analyse the implications for the field of academic nursing of its knowledge structure, drawing on social realist inspired developments in the sociology of education and, specifically, Maton's Legitimation Code Theory (LCT), which extends and integrates the work of Bourdieu and Bernstein. Irish academic nursing is used as an illustrative case to demonstrate the relevance and utility of this form of analysis and, in particular, of the concepts of knower and knowledge specialization, verticality, grammaticality, and semantic density and gravity. Conclusions: LCT provides a means of analyzing the knowledge structure of academic fields and assessing its implications for their progression and development. For academic nursing, LCT points to the need to focus on the stimuli that encourage cumulative and relevant knowledge building and to consider the reasons for the enduring appeal of knower-centred discourses.