Police subjectivities in South Africa: a discourse analysis of police officers’ talk on protest

Police officers in the South African Police Service (SAPS) undertake their police work within national, institutional, and personal discourses. Together, these discourses create different, often contradictory, police subjectivities. Resultantly, research on policing in South Africa is increasingly concerned with these subjectivities and the contexts in which they are constructed. However, despite this growing interest in discourse and subjectivities, scholars of policing have not typically employed a discourse analysis to examine these processes. Through a Foucauldian Discourse Analysis, we examine two discourses, violence as an internal malignancy of protest and protest as legitimate . The subjectivities enabled through these discourses both sympathised with and demonised the struggles of protesters, reflecting a broader contradiction in South African society, namely that protest is discursively reified in the Constitution but must be exercised within the discursive-material parameters set by the state

The Impact of Diversity in Disney's Ms. Marvel: An Analysis of YouTube Comments

This article examines the Disney+ streaming television series Ms. Marvel, as a case study in popular cultural representations of Muslim youth. By analysing YouTube comments on the shows trailer, this research explores how the series navigates the intersections of race, religion, gender, and Identity by drawing on primary social media data. Through a thematic analysis of YouTube comments, the study investigates the ways in which Ms. Marvel both challenges and reinforces dominant cultural discourses.. Themes identified include Culture and Identity where respondents appreciate the respectful depiction and cultural elements. Misrepresentation (34.2%) highlights concerns about the show’s inaccurate portrayal and criticisms that it caters more to Western audiences. Negative Views (17.4%) address stereotypes and criticisms of Marvel’s political motivations. Lastly, Unclassified Content (6.9%) encompasses responses lacking substantial content, often link-baiting or non-English text. A striking juxtaposition emerges between the show’s vibrant portrayal of a young Muslim woman and the significant volume of online hate speech and misogyny directed at it, highlighting the paradox of increased visibility and heightened vitriol. Ultimately, this research contributes to a broader understanding of how media representations influence identity formation and social change, particularly among marginalised communities. By investing in culturally grounded storytelling, producers can build a more loyal and diverse audience, fostering resonance and trust, which are essential for long-term viewer engagement and cross-cultural impact. The role of social media analysis, as demonstrated through the themes identified in YouTube comments, is an important medium for understanding audience perspectives on cultural representation in media

Augmenting safe system of working: a systems thinking approach with leading indicators embedded within

Complex and evasive phenomenon such as safety requires a holistic, multifaceted and intricately monitored and managed approach as opposed to current fragmented and reductionistic methods predominating in safety management. Such gestaltism of combining componential elements of complexly integrated systems can be achieved through the adoption of systems thinking and via the use of weak but early signals known as leading indicators. Therefore, this current doctoral study seeks to engender a novel theoretical basis in the form of conceptual model for the promulgation of proactive and holistic safety management, which is founded on continual and iterative learning from past and current safety activities. Such a conceptual model is inductively developed through analysis of existing knowledge in the literature and is tested with real life case study data. To achieve the research aim, the research philosophies of interpretivism and critical realism were adopted to study the phenomenon under investigation and develop new theoretical insights. Within this overarching epistemology, the research strategy of sequential mixed methods was employed by combining a systematic literature review and case study using combination of data analysis methods such as thematic analysis, content analysis, cross-comparison analysis and framework analysis. The research process follows two phases viz., in phase 1 pertinent literature is systematically reviewed with inductive reasoning and in phase 2 the research outcome from the preceding phase is tested with real case data using abductive and deductive reasoning. Consequently, the phase 1 of the study engenders a novel conceptual model for leading indicators’ development and implementation. To test this research outcome, a proof-of-concept is designed at phase 2 by adopting the development step of the conceptual model viz., by seeking to develop leading indicators from a combination of case study data and their relevant normative documents. In addition to testing the conceptual model, this step engenders a novel analytical framework which provides the systematic development of leading indicators from the qualitative dataset. As a result, a total of 484 new leading indicators were identified by using the analytical framework. Subsequently, all these three research outcomes (i.e. proof-of-concept model, analytical framework and examples of leading indicators) are validated through focus group interview of experts. Consequently, the study has developed multiple research outcomes, viz., main contributions such as proof-of-concept model in Figure 7.9; analytical framework in Figure 8.3; as well as other research contributions such as guidance note for training efficacy assessment in Figure 7.4; Safety-in-cohesion model in Figure 7.7; and Dynamic theory of incident evolution in Figure 8.5. These research findings generated create the groundwork for: proliferation of systems thinking in understanding safety, its management and maintenance; propagation of proactive and pre-emptive stance in development of safety countermeasures; and promulgation of a dynamic and adaptable approach in the generation of safety intelligence for continuous improvement. Therefore, these emergent theoretical and practical contributions stemming from this current doctoral work will become instrumental in mitigating asset and personal risks related to frontline workers’ interaction with operating vehicles and construction machinery on highway work sites as well as in other safety critical industries and sectors. Moreover, the work will be influential in continuously monitoring safety status of complex systems and simultaneously preventing unfavourable events from taking place and learning from both failures and successes

Hematology patients’ metaphorical perceptions of the disease and psychosocial support needs in the treatment process: a phenomenological study from a rural region of Türkiye

Purpose Although health services and access to these services have increased worldwide, there are still major barriers to access to health services, especially for rural, poor, and disabled individuals. The aim of this study was to evaluate the metaphorical perceptions of hematology patients living in rural areas in Türkiye about the disease and their psychosocial support needs during the treatment process with a phenomenological approach. Methods In this study, in which the phenomenological research method was used, semi-structured in-depth interviews were conducted with 14 hematology patients receiving treatment in a state hospital in a province in the eastern region of Türkiye. Criterion sampling method, one of the purposive sampling methods, was used to reach the sample group. Interviews continued until data saturation was achieved. The data of the study were evaluated using thematic analysis. The study was conducted and reported according to the COREQ checklist. Results Data analysis revealed two main themes including the following: metaphorical perceptions towards hematologic cancer with the sub-themes of emotional turmoil, social alienation and stigma and physical debilitation, and pathways to resilience with the sub-themes of disease process management, inner resilience and faith, and psychosocial support. Conclusion This study revealed that the adaptation process of patients to hematologic cancer is quite difficult, and psychosocial support is an indispensable requirement for them in their lives. In order for patients and their families to cope with this very difficult disease process, it is thought that providing them with a higher level of psychological support will be beneficial in coping with the disease effectively

Adaptive Intrusion Detection System with Ensemble Classifiers for Handling Imbalanced Datasets and Dynamic Network Traffic

Intrusion Detection Systems (IDS) are crucial for network security, but their effectiveness often diminishes in dynamic environments due to outdated models and imbalanced datasets. This paper presents a novel Adaptive Intrusion Detection System (AIDS) that addresses these challenges by incorporating ensemble classifiers and dynamic retraining. The AIDS model integrates K-Nearest Neighbors (KNN), Fuzzy c-means clustering, and weight mapping to improve detection accuracy and adaptability to evolving network traffic. The system dynamically updates its reference model based on the severity of changes in network traffic, enabling more accurate and timely detection of cyber threats. To mitigate the effects of imbalanced datasets, ensemble classifiers, including Decision Tree (DT) and Random Forest (RF), are employed, resulting in significant performance improvements. Experimental results show that the proposed model achieves an overall accuracy of 97.7% and a false alarm rate (FAR) of 2.0%, outperforming traditional IDS models. Additionally, the study explores the impact of various retraining thresholds and demonstrates the model's robustness in handling both common and rare attack types. A comparative analysis with existing IDS models highlights the advantages of the AIDS model, particularly in dynamic and imbalanced network environments. The findings suggest that the AIDS model offers a promising solution for real-time IDS applications, with potential for further enhancements in scalability and computational efficiency

Linking Organizational Dynamics to Digital Capabilities and ESG Outcomes: A Market-Oriented Approach

Environmental, Social, and Governance (ESG) represent a concept of sustainable development that integrates corporate governance, social responsibility, and environmental concerns. Most ESG research focuses on large-size publicly traded companies, and limited attention is paid to entrepreneurial firms. Our study explores how market orientation, organizational leadership, and organizational culture lead to digital capabilities and ESG performance of Chinese entrepreneurial organizations. This study outlines a comprehensive framework grounded on market orientation and resource-based theories. Using structural equation modeling, we empirically test our hypotheses where findings indicate that organizational leadership, culture, and market orientation positively influence both digital capabilities and ESG performance. Additionally, we observed that cultural factors insignificantly affect digital capabilities. This research contributes to understanding corporate sustainability for academics, stakeholders, regulators, and policymakers, offering key theoretical and practical insights for advancing corporate sustainability

Predictive Modelling of Incident Risk to Pre-empt Risk in Highway Operations: A Machine Learning Approach

Highway traffic officers (HTOs) operate in complex and hazardous environments, yet transportation safety research has predominantly focused on drivers, pedestrians, roads, and vehicles, with limited attention to HTOs' safety. In the UK, National Highways currently employs traditional statistical methods to mitigate safety risks post-incident, a reactive approach that does little for risk prevention. This thesis proposes a proactive approach by developing a machine learning (ML) prediction model to forecast incidents such as injuries, incursions and environmental hazards, assess risk levels, and predict the body parts likely to be affected in injurious events. The aim is to provide highway safety authorities with predictive insights for timely interventions and enhanced risk management. Despite the growing application of ML in safety risk prediction, there is limited evidence on the reliability of variables used as indicators of safety performance. To address this gap, this study develops a conceptual framework for selecting optimal safety indicators (SIs) and formulating input variables that enhance ML-based risk prediction. A three-stage, multiphase mixed-methods research design was employed: i) developing the conceptual framework; ii) constructing the proof-of-concept ML model; and iii) validating the model’s performance. The conceptual framework was established through a systematic literature review using PRISMA-based bibliometric search, scientometric and cluster analysis to identify significant SIs and grounded theory analysis was used for synthesis. The ML model development phase applied supervised learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Deep Neural Networks (DNN), Ensemble Learning (EL), and Recurrent Neural Networks (RNN). The models were trained using secondary data from a highway incident database and three data balancing techniques were tested to address the class imbalance. Model validation employed a stratified k-fold cross-validation approach, evaluated based on AUROC, precision, recall, and accuracy. The study identifies key considerations for selecting SIs, emphasizing the integration of leading and lagging indicators to enhance system adaptability and resilience. A novel conceptual framework is presented that guides the selection of robust indicators for ML-based risk modelling. Empirical findings indicate that the SVM model with a polynomial kernel, combined with the SMOTE algorithm, outperforms other models in predicting incident types, risk levels, and affected body parts, whereas Random Under-sampling (RU) was the least effective. Critical factors influencing highway incidents, including weather conditions, visibility, age range and location, were identified and analysed. This research makes several novel contributions: i) a novel conceptual framework integrating resilient SIs for predictive modelling; ii) a systematic approach to combining leading and lagging indicators for enhanced safety performance; and iii) the first study to use an incident database dedicated to HTOs for predictive risk modelling. The developed ML model provides actionable insights for safety officers, enabling proactive risk mitigation through targeted training and preparedness strategies for HTOs. This ultimately improves workplace safety in highway operations

From informal to formal: Does formal enterprises’ prior experience in the informal sector drive their adoption of bricolage?

While previous studies have significantly contributed to our understanding of bricolage – an important resource mobilisation approach in resource-constrained contexts, there is limited research on the impact of formal enterprises’ prior informal sector experience on their adoption of bricolage. This paper develops and tests the hypothesis that the adoption of bricolage can be a function of prior informal sector experience. Using survey data from 1251 formal enterprises operating in Indonesia, we found support for our proposition. Our findings show that there is a positive relationship between prior informal sector experience and the adoption of bricolage. This relationship is strengthened by greater levels of informal competition. The results have implications for theory and practice

A cross-sectional study exploring the sitting time of afghans and other South Asian youth in the UK

Introduction Prolong sitting time (ST) contributes to obesity and numerous Non-Communicable Diseases including type 2 diabetes and cardiovascular diseases. Concerning evidence on young people’s health has reported an increase in ST, the young British South Asians (BSA) is under explored. Thus, the aim of this study was to explore the ST of BSA community, specifically focusing on Afghans, Pakistani, Bangladeshi, and Indian groups. Methods Young BSA from the UK West Midlands region (mean age 15.4 ± 0.5 years) (Total: n = 191, (females: n = 93; males: n = 98) participated in this study. ST was measured based on self-reported total sitting hours using The International Physical Activity Questionnaire—Short Form. Data were modelled using a Bayesian approach to determine differences in ST. Results The findings indicated that the majority of the BSA young people across ethnicities spent prolonged time being sitting. Young people from Indian ethnicity had the highest estimated marginal mean ST (482.23. 95% CI [410.49, 554.73]) and the Afghans the lowest estimated marginal mean ST (344.61, 95% CI [280.22, 411.33]). Discussion This study emphasised a worryingly high percentage of young people from each BSA ethnic group spending prolong ST. To the authors’ knowledge, this is the first study to explore and compare the inter-population differences in ST within BSA minority ethnicities, including Afghan population in the UK. Conclusion The present findings provide a rationale for further scrutiny on key objective and qualitative determinants contributing to ST within different ethnicities among BSA young people