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Imbalances of the past: marginalisation of women in leadership roles in South African higher education.
(2023) Zungu, Snenhlanhla Ntomfuthi.; Mnisi, Thoko Esther.
The gender equality policy encourages the full and equal participation of women in the workplaces. However, there has been a significant dearth of women in South Africa senior leadership roles. The main aim of the study is to explore the roles of social capital in promoting women into senior leadership positions in higher education institutions. I have drawn social capital as a theoretical framework to analyse data to understand the impact of social capital in advancing women into senior leadership. The study is qualitative. I used the semi-structured interviews to generate data. Three
women who were school deans were interviewed. The interview questions were constructed to answer these critical questions of the study: What influence does the social capital have in advancing women to senior leadership positions in higher education? How significant are the professional networks in contributing to career progression of women leaders in higher education? How can the aspiring women leaders be supported by women who have ascended to leadership position in universities? What do women leaders recommend for women who desire to be in senior leadership roles? Thematic analysis was used to analyse the qualitative data to look for patterns in the meaning of data to find themes. The findings suggest the four key points: The influence of social capital in advancing women to senior leadership, professional networks in career advancement of women leaders, importance of supporting aspiring women leaders by the experienced women leaders and insight gained by experiences of women leaders. The study suggested the following recommendations for the higher education institutions in South Africa: (1) The reviewing of the promotion criteria to accommodate women as they have dual responsibilities between work and family, and (2) Continuing mentorship of aspiring women leaders to increase their job proficiency.
Young men negotiating masculinities and love in a South African township.
(2023) Dlamini, Melusi Andile Charles.; Bhana, Deevia.
Young black men’s negotiations of love and intimacy, beyond the focus on force and violence, are minimally explored in South African scholarship. While studies have highlighted the ways that heterosexual relationships have functioned as sites through which men maintain their dominance over women, there is limited understanding of the ways that they resist dominant masculinities. Furthermore, recent scholarship has troubled the reductive readings of young black men’s lives, and have called for critical yet sympathetic approaches to understanding their lived experiences (Ratele, 2018). Therefore, this study explores how young black men, situated in the townships of Durban, navigate their experiences of romantic love and intimate relationships. Informed by critical feminist approaches to love and masculinities, this study emphasises the multiple and situated ways of being and knowing, and challenges reductive readings of young men’s lives. Empirical data were generated through individual interviews and focus group discussions with 34 young men in the INK (Inanda, Ntuzuma, KwaMashu)
precinct of townships in Durban, South Africa. The research findings suggest that romantic love and intimate relationships are an important feature of young men’s daily lives. For most of the participants, romantic love and intimate relationships extended beyond public performances of (hetero)sexual prowess; instead, love was understood as an essential aspect of their shifting subjectivities – from boyhood to manhood. Key relational practices such as ukuchecka, which are often associated with public performances, emerged as important sites through which participants cultivated emotional and physical intimacies. Among the participants, their romantic relationships afforded new ways of expressing love, which enabled them to deemphasise sexual intimacy, which the participants expressed through the concepts of ukuhloniphana (mutual respect) and ukulinda (waiting). Specifically, romantic relationships were also conceptualised as affective sites that enabled the young men to co-navigate their daily lives with their girlfriends. Therefore, in this study, the critical and situated reading of young men’s experiences with love generated new knowledge about their expressions of love and experiences of intimate relationships. Typically thought of as a site of women’s
vulnerabilities, these findings suggest that the context of romantic love offers progressive possibilities for young men to resist dominant masculinities. This study illustrates the value of exploring the mundane, everyday encounters of love and intimate relationships in young men’s
lives. These findings have implications for local and international masculinities scholarship interested in the transformative possibilities of love and intimate relationships in young men’s lives.
Statistical and machine learning methods of online behaviours analysis.
(2024) Soobramoney, Judah.; Chifurira, Retius.; Zewotir, Temesgen Tenaw.
The success of corporates is highly influenced by the effectiveness and appeal of each corporate’s website. This study was conducted on TEKmation, a South African corporate, whose board of directors lacked insight regarding the website’s usage. The study aimed to quantify the web-traffic flow, detect the underlying browsing patterns, and validate the web-design effectiveness. The website experienced 7,935 visits and 57,154 page views from 1 June 2021 to 30 June 2023 (data sourced by Google Analytics). Grubb’s test has identified outliers in visit frequency, the pageviews per visit, and the visit
duration per visit. A small degree of missingness was observed on the mobile device branding (1.24%) and operating system (0.03%) features which were imputed using a Bayesian network model. To address a data-shift detected, an artificial neural network (ANN) was proposed to flag future data-shifts with important predictors being the period of year and volume of sessions. Prior to clustering, feature selection methods assessed the feature variability and feature association. Results indicated that low-incidence webpages and features with natural relationships should be omitted. The K-means, DBScan
and hierarchical unsupervised machine learning methods were employed to identify the visit personas, labelled get-in-touch (12%), accidentals (11%), dropoffs (30%), engrossed (38%) and seekers (9%). It was evident that the premature drop-offs needed further exploration. The Cox proportional hazards survival model and the random survival forest (RSF) model have identified that the web browser, visit frequency, device category, distance, certain webpages, volume of hits, and organic searches proved to be drop-offs hazards. A tiered Markov chain model was developed to compute the transition probabilities of dropping-off. The contact (63%) and clients (50%) states recorded a high likelihood to drop-off early within the visit. In conclusion, using statistical methods, the study informed the board on of its audience, the flaws of the website and proposed recommendations to address concerns.
Stable distributions with applications to South African financial data.
(2024) Naradh , Kimera.; Chinhamu, Knowledge.; Chifurira, Retius.
In recent times, researchers, analysts and statisticians have shown a keen interest
in studying Extreme Value Theory (EVT), particularly with the application to
mixture models in the medical and financial sectors. This study aims to validate the
use of stable distributions in modelling three Johannesburg Stock Exchange (JSE)
market indices, namely the All Share Index (ALSI), Banks Index and the Mining
Index, as well as the United States of American Dollar (USD) to South African
Rand (ZAR) exchange rate. This study leverages the unique properties of stable
distributions when modelling heavy-tailed data. Nolan’s S0-parameterization
stable distribution (SD) was fitted to the returns of the three FTSE/JSE indices and
USD/ZAR exchange rate and a hybrid Generalized Autoregressive Conditional
Heteroskedasticity (GARCH)-type model combined with stable distributions was
fitted to each return series. The two-tailed mixture model of the Generalized Pareto
Distribution (GPD), stable distribution, Generalized Pareto Distribution referred to
as GSG, as well as the Stable-Normal-Stable (SNS) and Stable-KDE-Stable (SKS) was
fitted to evaluate its relative performance in modelling financial data. Results show
that the S0-parameterization SD fits the South African financial returns well. The
hybrid GARCH (1,1)-SD model competes favourably with the GARCH-GPD model
in estimating Value-at-Risk (VaR) for FTSE/JSE Banks Index, FTSE/JSE Mining Index
and the USD/ZAR exchange rate returns. The hybrid EGARCH (1,1)-SD competes
well against the GARCH-GPD model for the FTSE/JSE ALSI returns. Inconclusive
results are observed for the short position of the fitted GKG models; however, in the
long position, an appropriate fit of the GPD-KDE-GPD (GKG) model, where KDE is
the kernel density estimator, is emphasised for all four return series. The proposed
mixture models, GSG, SNS and SKS models, are found to be a good alternative in
fitting South African financial data to the commonly used GPD-Normal-GPD (GNG)
mixture model. The results of this study are important to financial practitioners, risk
managers and researchers as the proposed mixture models add more value to the
literature on the applications of extreme mixture models.
Road obstacle detection Using YOLO algorithm based on attention mechanism.
(2024) Lekola , Bafokeng.; Viriri, Serestina.
Road obstacle detection is an important task in autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) as they require real-time operation and high accuracy for safe operation. The mobile nature of the task means that it is carried out in a low-resourced environment where there is a need for an algorithm that achieves both high accuracy and high inference speed while meeting the requirement for lightweight. In this dissertation, an exploration of the effectiveness of the Attention-enhanced YOLO algorithm for the task of road obstacle detection is carried out. Several state-of-the-art
attention modules that employ both channel and spatial attention are explored and fused with the YOLOv8 and YOLOv9 algorithms. These enhance feature maps of the network by suppressing non-distinctive features allowing the network to learn from highly distinctive features. The Attention-modified networks are trained and validated on the Kitti and BDD100k datasets which are publicly available. Comparisons are made between the models and the baseline. An improvement from the baseline is seen with the GAM attention achieving an accuracy rate of 93.3% on the Kitti dataset and 71.1% on the BDD100k
dataset. The Attention modules generally achieved incremental improvements over the baseline.