Daisy Jacobs

Capacitating national research: a review of South African natural sciences research projects, theses and dissertations, 1986-2006

The paper examines selected natural sciences research output in South Africa, with special reference to research projects, theses and dissertations in order to determine the trends and nature of capacitation of national research in the selected disciplines in the country. Relevant data was extracted from the South African Bibliographic and Information Network (SABINET)-hosted Current and Completed Research (C&CR) database using subject codes as well as subject keywords. The study reveals that the trends of capacitating national research in natural sciences in South Africa follows a mixed pattern of growth, biology is the most researched subject/discipline, co-supervision of research projects in South Africa is minimal, universities are the main centres for capacitating national research, interdisciplinary and multidisciplinary research is visible, research capacity building is largely at the level of Masters degree and English language is the most commonly used language of capacitation of national research. In conclusion, we argue, among other recommendations, that there is a need to convert the completed research projects (that is, technical reports, dissertations, theses and so on) into publishable research articles for wider dissemination of research findings.

An informetric analysis of publication and research collaboration patterns in natural and applied sciences in South Africa

This study is the part of an ongoing research which presents the mainstream scientific output and collaboration of five research universities of South Africa over a 9 year period between 1995 and 2003. Since a part of this research has already been published in 2006 where the main emphasis was on publication output, this paper concentrates more on collaboration. The paper discusses the distribution of publications by institutions, index of specialization, collaboration and patterns of co-authorship. The results show that South African authors collaborated more frequently with international authors (73.99%) than with national authors (26.01%). This was confirmed statistically at a confidence level of p-value < 0.025. A further non-parametric chi-square statistical analysis illustrated that there are significant differences in the proportion of co-authorship amongst the five institutions (p-value < 0.005). The results obtained shows that there is a sharp decline in publication output from 1995 until the end of 1998 and then again from 2003. The decrease in publication output is also an indication of the lack of collaborative research by South African scientists.

Knowledge Management Solution to Challenges of Higher Education in South Africa

This article presents challenges facing higher education in South Africa and how knowledge management can be applied to ameliorate the situation. Some of these challenges include internal and external pressures for accountability and transparency in the management of the institutions; declining state subsidies; stiff competition from global counterparts; low graduate throughput; declining enrolments; inadequate facilities (e.g. space, ICTs and equipment); ill-prepared graduates for the job market; limited partnership with industry and government; brain drain; bureaucracy and general poor service delivery. The authors submit that South African universities have largely not embraced knowledge management practices and argue that KM integration within the universities’ strategic processes and operations can help address the challenges facing them. The article is largely based on authoritative secondary and primary sources complemented by the authors’ experiences working within university environments in Southern Africa.

Transaction clustering of web log data files using genetic algorithm

Increasingly web applications found to impact on numerous environments. The web log data offer more promises and particularly application of the genetic algorithms is significant as it represents the relations between different data components. We have used simple genetic algorithms to log files and we found that the preliminary results are more promising there by open more avenues for future research.