Does a Management Degree Do the Business for Women in the Graduate Labour Market?

The increasing dominance of an economic ideology of higher education, that its principal role is to contribute to national competitiveness, has increased focus on the employability of graduates and their transition into the labour market. Drawing on a major study of the early career paths of 1995 graduates from 38 UK higher education institutions, this paper looks specifically at graduates from business and management programmes. Such graduates would apparently be attractive to employers seeking to recruit degree holders with specific transferable skills who might be expected to ‘hit the ground running’ once in organisations. The paper seeks to address the question of what effect holding a business degree has on the employment outcomes in comparison with other degree disciplines and whether or not there is any gender effect upon relative success in the labour market.     

The Management and Impact of a Student Led Iraq War Protest in a Fresh Start School

A student led Iraq war protest in a Fresh Start school provides a case study for examining the relationship between political literacy and institutional change. Pragmatic and creative management of the protest encourages a democratic problem-solving process, which contributes to mutual respect and sustained trust between pupils, and staff and pupils. There is evidence to suggest that this democratic experience within the school has the potential to bring a democratic problem solving ethos into the classroom, where learning may increasingly become characterized by individuals achieving autonomy with and for each other.     

Applying Machine Learning to Text Segmentation for Information Retrieval

We propose a self-supervised word segmentation technique for text segmentation in Chinese information retrieval. This method combines the advantages of traditional dictionary based, character based and mutual information based approaches, while overcoming many of their shortcomings. Experiments on TREC data show this method is promising. Our method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive. We find that although the segmentation accuracy of self-supervised segmentation is not as high as some other segmentation methods, it is enough to give good retrieval performance. It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. However, for Chinese, we find that the relationship between segmentation and retrieval performance is in fact nonmonotonic; that is, at around 70% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44% to 95%, including 70% word segmentation accuracy from our self-supervised word-segmentation approach. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text. Our research suggests machine learning techniques can play an important role in building adaptable information retrieval systems and different evaluation standards for word segmentation should be given to different applications.