11,475 research outputs found
Diversifying academic and professional identities in higher education: some management challenges
This paper draws on an international study of the management challenges arising from diversifying academic and professional identities in higher education. These challenges include, for instance, the introduction of practice-based disciplines with different traditions such as health and social care, the changing aspirations and expectations of younger generations of staff, a diffusion of management responsibilities and structures, and imperatives for a more holistic approach to the "employment package", including new forms of recognition and reward. It is suggested that while academic and professional identities have become increasingly dynamic and multi-faceted, change is occurring at different rates in different contexts. A model is offered, therefore, that relates approaches to "people management" to different organisational environments, against the general background of increasing resource constraint arising from the global economic downturn
Managing Human Resources in Higher Education: The Implications of a Diversifying Workforce
Human resource capacity has become a critical issue for contemporary universities as a result of increasing pressures from governments and global markets. As a consequence, particularly where the institution is the employer, changes are occurring in the expectations of staff and institutions about employment terms and conditions, as well as the broader aspects of working life, and this is affecting academic and professional identities. Even under different regimes, for instance, in Europe, with the government in effect as the employer, institutions are giving greater attention to ways in which they might respond to these developments. This paper considers key issues and challenges in human resource management in higher education, and some of the implications of these changes
Social representations: a revolutionary paradigm?
Against the prevailing view that progress in science is characterized by the progressive accumulation of knowledge, Thomas Kuhnâs Structure of Scientific Revolutions of 1962 introduced the idea of revolutionary paradigm shifts. For Kuhn, everyday science is normal science in which scientists are engaged in problem solving activities set in the context of a widely accepted paradigm that constitutes a broad acceptance of a fundamental theoretical framework, an agreement on researchable phenomena and on the appropriate methodology. But, on occasions normal science throws up vexing issues and anomalous results. In response, some scientists carry on regardless, while others begin to lose confidence in the paradigm and look to other options, namely rival paradigms. As more and more scientists switch allegiance to the rival paradigm, the revolution gathers pace, supported by the indoctrination of students through lectures, academic papers and textbooks. In response to critics, including Lakatos who suggested that his depiction reduced scientific progress to mob psychology, Kuhn offered a set of criteria that contributed to the apparent âgestalt switchâ from the old to the new paradigm. But that is another story, as indeed is Kuhnâs claim that the social sciences are pre-paradigmatic â in other words, that the only consensus is that there is no consensus
Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service
In the near future FPGAs will be available by the hour, however this new
Infrastructure as a Service (IaaS) usage mode presents both an opportunity and
a challenge: The opportunity is that programmers can potentially trade
resources for performance on a much larger scale, for much shorter periods of
time than before. The challenge is in finding and traversing the trade-off for
heterogeneous IaaS that guarantees increased resources result in the greatest
possible increased performance. Such a trade-off is Pareto optimal. The Pareto
optimal trade-off for clusters of heterogeneous resources can be found by
solving multiple, multi-objective optimisation problems, resulting in an
optimal allocation of tasks to the available platforms. Solving these
optimisation programs can be done using simple heuristic approaches or formal
Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial
options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore
CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up
to 110% faster than a heuristic approach, and over 50% cheaper. These results
suggest that high quality performance-resource trade-offs of heterogeneous IaaS
are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software
Programmers (FSP 2015) (arXiv:1508.06320
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