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This chapter considers the impetus for the inclusion of labour rights and secure work rights, with a particular focus on countering human trafficking and what is now widely known as ‘modern slavery’ in the UN Sustainable Development Goals (SDGs). The SDGs comprise 17 goals and 169 targets set to assist nation states in achieving sustainable development in the ‘five P’ areas: People, Planet, Prosperity, Peace and Partnership. In this chapter we analyse goals and targets that focus on modern slavery and adult human trafficking (in particular sex trafficking and trafficking for forced labour), and review the SDGs in the context of existing international counter-trafficking and slavery mechanisms. We consider what this novel framework has to offer when it comes to addressing these forms of exploitation. In so doing, the chapter considers the likely impact of the SDGs to preventing and countering these exploitative practices, and its potential usefulness within the broader spectrum of counter-trafficking/slavery mechanisms. We suggest that the SDGs are yet another international instrument that makes strong rhetorical commitments to the intersections of labour, migration and exploitation, but lacks clarity and operational strength it needs to lead the path in reduction, if not elimination of such exploitative practices. Finally, we analyse the extent to which this instrument continues to ignore the factors that contribute to or sustain the conditions for exploitation, namely the impact of migration policies and the gendered nature of the issue.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

How do transnational social movements organize? Specifically, this paper asks how an organized community can lead a nationalist movement from outside the nation. Applying the analytic perspective of Strategic Action Fields, this study identifies multiple attributes of transnational organizing through which expatriate communities may go beyond extra-national supporting roles to actually create and direct a national campaign. Reexamining the rise and fall of the Fenian Brotherhood in the mid-nineteenth century, which attempted to organize a transnational revolutionary movement for Ireland’s independence from Great Britain, reveals the strengths and limitations of nationalist organizing through the construction of a Transnational Strategic Action Field (TSAF). Deterritorialized organizing allows challenger organizations to propagate an activist agenda and to dominate the nationalist discourse among co-nationals while raising new challenges concerning coordination, control, and relative position among multiple centers of action across national borders. Within the challenger field, “incumbent challengers” vie for dominance in agenda setting with other “challenger” challengers.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Higher education (HE) in England and other parts of the United Kingdom (UK), traditionally and historically, has been dominated by privileged and powerful social groups. In recent decades, universities have opened their doors and encouraged participation by a diversity of learners including women, working class, minority ethnic groups and many others that might be deemed historically under-represented in HE. This movement came to be known as ‘widening participation’. I consider myself to be a product of the widening participation movement having returned to learn in 1994 after a 10-year break in education. However, providing access to participate is only the first step. For many HE students from under-represented groups, like the working class, the journey through the academy, while earning their degree, can be fraught with profound and difficult experiences. This chapter charts my own journey into HE as a student, and back into HE as an academic, with some equally fraught and profound experiences.