PurposeThis paper aims to examine how multinational enterprises minimise tax liabilities in emerging economies through lawful but engineered cross border structures. Using Rawlsian distributive justice, it assesses whether international tax rules and court responses protect the least advantaged by preventing tax base erosion that reduces funding for essential public services. It also examines how professional intermediaries enable complexity and shape the legitimacy of the international tax order.Design/methodology/approachDoctrinal analysis of judgments from India, Nigeria, Ghana and South Africa is combined with socio legal critique. The cases are mapped to four Rawls grounded assumptions on fairness, fragmentation, transparency and accountability.FindingsThe cases show repeatable avoidance mechanisms. In India, offshore restructuring treated a value shifting transfer as exempt. In Nigeria, layered group structures and joint venture operations complicated tax enforcement while harm litigation moved abroad. In Ghana, offshore routed payments reduced local scrutiny around public contracts. In South Africa, structured finance re-characterised interest as exempt dividends until challenged under anti avoidance rules. Across contexts, advisers and intermediaries supported opacity and individual accountability was limited. This increases pressure on low-income states with limited institutional capacity. Judicial reasoning shaped outcomes because purposive interpretation protected the tax base, whereas formalism accepted legal form and allowed base erosion that fails a Rawlsian fairness test.Research limitations/implicationsThe focus on four jurisdictions limits generalisation but provides deep insight into ethical and legal asymmetries in global taxation.Practical implicationsPolicymakers should adopt substance based anti-avoidance rules and enforce adviser duties, with promoter disclosure, sanctions and mutual assistance.Originality/valueThe study links Rawlsian justice to comparative case law and proposes UN reforms that target professional enablers of tax avoidance.
COVID-19 still poses a global public health challenge, exerting pressure on radiology services. Chest X-ray (CXR) imaging is widely used for respiratory assessment due to its accessibility and cost-effectiveness. However, its interpretation is often challenging because of subtle radiographic features and inter-observer variability. Although recent deep learning (DL) approaches have shown strong performance in automated CXR classification, their black-box nature limits interpretability. This study proposes an explainable deep learning framework for COVID-19 detection from chest X-ray images. The framework incorporates anatomically guided preprocessing, including lung-region isolation, contrast-limited adaptive histogram equalization (CLAHE), bone suppression, and feature enhancement. A novel four-channel input representation was constructed by combining lung-isolated soft-tissue images with frequency-domain opacity maps, vessel enhancement maps, and texture-based features. Classification was performed using a modified Xception-based convolutional neural network, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to provide visual explanations and enhance interpretability. The framework was evaluated on the publicly available COVID-19 Radiography Database, achieving an accuracy of 95.3%, an AUC of 0.983, and a Matthews Correlation Coefficient of approximately 0.83. Threshold optimisation improved sensitivity, reducing missed COVID-19 cases while maintaining high overall performance. Explainability analysis showed that model attention was primarily focused on clinically relevant lung regions.
Faculty retention has become a strategic concern for universities facing mounting competitive, financial, and reputational pressures. This study examines how employee engagement, job embeddedness, and perceived organizational support relate to faculty turnover intentions, framing retention as a strategic capability rooted in everyday organizational practices. Using a cross-sectional survey of university faculty in the United States and structural equation modeling, the study compares the relative influence of engagement, embeddedness, and organizational support on turnover intentions. The findings indicate that employee engagement and perceived organizational support are strongly and negatively associated with turnover intentions, with engagement partially mediating the relationship between support and intention to leave. Job embeddedness is measured reliably but displays a weaker and at times nonsignificant direct effect, suggesting that relational ties and fit alone are insufficient in shaping retention decisions absent energized and supported work. No substantive differences emerge across academic rank or discipline. The results underscore the strategic value of fostering engagement through recognition, autonomy, and effective workload design, alongside visible organizational support mechanisms such as mentoring and career development. Embeddedness initiatives add value when integrated into broader strategies that enhance meaningful work and organizational support, reinforcing faculty retention as a component of long-term institutional resilience.
What teachers do in their classrooms is underpinned by their personal and professional values: this is their teacher agency. Despite this, some suggest that novice teachers can struggle to teach in a way which reflects the values and beliefs which are important to them, hindering their teacher agency. This article is a hopeful one in that it argues that those at the start of their teaching careers can engage in small acts of resistance which can be identified as 'truth-telling'. By engaging in fearless speech, the novice teachers in this small-scale qualitative study demonstrated an ability to do what it was they thought they should be doing, to exert and develop their sense of teacher agency, despite the potential for risks to their reputations or positions within their schools. The article concludes by advocating for more truth-telling, especially for those working within current Initial Teacher Education (ITE) in England.
The incidence of chronic subdural haematoma (CSDH) is rising in the elderly, yet there is limited research focusing on patients aged > 80. In the United Kingdom, the decision to transfer for neurosurgical intervention is dependent upon the acceptance of a CSDH management referral by a tertiary centre. This study thus aims to identify key predictors of referral acceptance in octogenarian and nonagenarian age groups, in comparison to the adult population. A multi-centre retrospective case series analysis from January 2015 to May 2020 was conducted. Patients were grouped as < 80 (‘adult’) or > 80 (‘ultra-geriatric’), with the latter further subgrouped into 80–89 (‘octogenarian’), and ≥ 90 (‘nonagenarian’). Multivariable logistic regression assessed age, headache, dementia, motor weakness, midline shift, CSDH size, and premorbid quality of life (QoL) to determine predictors of referral acceptance. With a total of 3002 patient referrals, referral acceptance was significantly higher in adults compared to the ultra-geriatric group (38.1
