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Applied Science (applied + science)
Selected AbstractsShould governments fund science?ECONOMIC AFFAIRS, Issue 3 2000Terence Kealey Empirical evidence shows the flaws in the ,linear model' of economic growth - in which government funds pure science which leads to applied science and enhanced economic growth. Adam Smith's model - in which academic science flows out of applied science - is nearer the mark. Governent funding of science cannot be justified on economic grounds and indeed tends to crowd out private funding. [source] Plaud on Ilardi and FeldmanJOURNAL OF CLINICAL PSYCHOLOGY, Issue 9 2001Joseph J. Plaud A cultural analysis is given to the premise that clinical psychology is a discipline of theoretical fragmentation. It is argued that the discipline will achieve paradigmatic status as an applied science by reestablishing the link between behavioral theory and clinical applications. © 2001 John Wiley & Sons, Inc. J Clin Psychol 57: 1109,1111, 2001. [source] The Bridge to the ,Real World': Applied Science or a ,Schizophrenic Tour de Force'?*JOURNAL OF MANAGEMENT STUDIES, Issue 6 2004Alexander T. Nicolai abstract This article concerns those publications which have received considerable attention in an academic as well as in a practical context. In these rare cases, it seems that it was possible to transfer scientific findings more or less directly into managerial implications. This widely shared view is contrasted with a socials systems perspective. From this point of view there cannot be a direct application of scientific knowledge. This also holds true for the classic examples of applied science. It is argued that even in these cases there is no evidence of linear knowledge transfer but rather ,Applied Science Fiction' (ASF). ASF comprises all techniques with which the scientific system reacts to external application pressure without having to relinquish its own self-referential logic. Different forms of ASF are introduced. These are retrofitting, reputation, symbolic labels and undisciplined eclecticism. The ASF-concept will be illustrated by Michael Porter's Competitive Strategy. Paradoxically, however, the conventional concept of application and ASF are a barrier for the sustainable relevance of management studies. [source] How can Rorty help nursing science in the development of a philosophical ,foundation'?NURSING PHILOSOPHY, Issue 2 2009Sandy Isaacs BScN MSc RN Abstract What can nurse scientists learn from Rorty in the development of a philosophical foundation? Indeed, Rorty in his 1989 text entitled Contingency, Irony, and Solidarity tantalizes the reader with debates of reason ,against' philosophizing. Forget truth seeking; move on to what matters. Rorty would rather the ,high brow' thinking go to those that do the work in order to make the effort useful. Nursing as an applied science, has something real that is worth looking at, and that nurse researchers need to think about. And as a profession built upon relationships, we should be thinking of the exchanges we have with those around us, of the contrasts in vocabularies used and of the contingencies involved, letting this launch us into our imaginings and areas of enquiry. The business of nurse researchers is to study what nurses do , how we care; Rorty would have us care. But, not to dismiss the reflective thinker as Rorty advocates for the self-doubting ironist to continue to seek the final vocabulary, the ideal of what ,this' means, accepting this as the best to be offered at the time. As a science struggling to find foundation, we need only to look at what we do and value , as antifoundational as Rorty portrays himself, Rorty ,ironically' may have revealed a foundation for nursing science that is consistent with its path. [source] Balancing the Need to Develop Coastal Areas with the Desire for an Ecologically Functioning Coastal Environment: Is Net Ecosystem Improvement Possible?RESTORATION ECOLOGY, Issue 1 2005R.M. Thom Abstract The global human population is growing exponentially, close to a majority lives and works near the coast, and coastal commerce and development are critical to the economies of many nations. Hence, coastal areas will continue to be a major focus of development and economic activity. People desire the economic advantages provided by coastal development along with the fisheries and social commodities supported by estuarine and coastal ecosystems. Because of these facts, we view the challenge of balancing coastal development with enhancing nearshore marine and estuarine ecosystems (i.e., net ecosystem improvement) as the top priority for coastal researchers in this century. Our restoration research in Pacific Northwest estuaries and participation in nearshore project design and impact mitigation has largely dealt with these competing goals. To this end, we have applied conceptual models, comprehensive assessment methods, and principles of restoration ecology, conservation biology, and adaptive management to incorporate science into decisions about uses of estuarine systems. Case studies of Bainbridge Island and the Columbia River demonstrate the use of objective, defensible methods to prioritize tidally influenced shorelines and habitats (i.e., riparian forests, marshes, unvegetated flats, rocky shores, seagrass meadows, kelp forests) for preservation, conservation, and restoration. Case studies of Clinton, Washington, and Port Townsend, Washington, demonstrate the incorporation of an ecological perspective and technological solutions into design of overwater structures to minimize impacts on nearshore ecosystems. Adaptive management has allowed coastal development and restoration uncertainties to be better evaluated, with the information used to improve management decisions. Although unproven on a large scale, we think these kinds of methods can contribute to the net improvement of already degraded ecosystems. The ingredients include applied science to understand the issues, education, incentives, empirical data, cumulative impact analysis, and an effective adaptive management program. Because the option of net ecosystem improvement is often more costly than alternatives such as no net loss, commitment by the local or regional community to this approach is essential. [source] Measuring Efficiency: A Comparison of Multilevel Modelling and Data Envelopment Analysis in the Context of Higher EducationBULLETIN OF ECONOMIC RESEARCH, Issue 2 2006Jill JohnesArticle first published online: 15 MAR 200 I21; C14; C16 Abstract Data envelopment analysis (DEA) and multilevel modelling (MLM) are applied to a data set of 54,564 graduates from UK universities in 1993 to assess whether the choice of technique affects the measurement of universities' performance. A methodology developed by Thanassoulis and Portela (2002; Education Economics, 10(2), pp. 183,207) allows each individual's DEA efficiency score to be decomposed into two components: one attributable to the university at which the student studied and the other attributable to the individual student. From the former component, a measure of each institution's teaching efficiency is derived and compared to the university effects from various multilevel models. The comparisons are made within four broad subjects: pure science, applied science, social science and arts. The results show that the rankings of universities derived from the DEA efficiencies which measure the universities' own performance (i.e., having excluded the efforts of the individuals) are not strongly correlated with the university rankings derived from the university effects of the multilevel models. The data were also used to perform a university-level DEA. The university efficiency scores derived from these DEAs are largely unrelated to the scores from the individual-level DEAs, confirming a result from a smaller data set (Johnes, 2006a; European Journal of Operational Research, forthcoming). However, the university-level DEAs provide efficiency scores which are generally strongly related to the university effects of the multilevel models. [source] H-methods in applied sciencesJOURNAL OF CHEMOMETRICS, Issue 3-4 2008Agnar Höskuldsson Abstract The author has developed a framework for mathematical modelling within applied sciences. It is characteristic for data from ,nature and industry' that they have reduced rank for inference. It means that full rank solutions normally do not give satisfactory solutions. The basic idea of H-methods is to build up the mathematical model in steps by using weighing schemes. Each weighing scheme produces a score and/or a loading vector that are expected to perform a certain task. Optimisation procedures are used to obtain ,the best' solution at each step. At each step, the optimisation is concerned with finding a balance between the estimation task and the prediction task. The name H-methods has been chosen because of close analogy with the Heisenberg uncertainty inequality. A similar situation is present in modelling data. The mathematical modelling stops, when the prediction aspect of the model cannot be improved. H-methods have been applied to wide range of fields within applied sciences. In each case, the H-methods provide with superior solutions compared to the traditional ones. A background for the H-methods is presented. The H-principle of mathematical modelling is explained. It is shown how the principle leads to well-defined optimisation procedures. This is illustrated in the case of linear regression. The H-methods have been applied in different areas: general linear models, nonlinear models, multi-block methods, path modelling, multi-way data analysis, growth models, dynamic models and pattern recognition. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||