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 Kuhn’s 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 ‘gestalt switch’ from the old to the new paradigm. But that is another story, as indeed is Kuhn’s claim that the social sciences are pre-paradigmatic – 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