‘Remote work’ and ‘telework’, which used to be regarded as exceptional subcategories of labor engagement, became the norm for white collar workers during the pandemic. Recent years have seen the advent of hybrid labor arrangements, where work is directly or indirectly provided through apps or similar pieces of software and other technological innovations. The overarching work digitization phenomenon is defined by increasing delocalisation and fragmentation of workplaces, and by algorithmic management. Even work typically performed on-site includes nowadays elements of delocalization. This chapter revisits our understanding of ‘teleworking’ and examines the appropriateness of existing collective labor law institutions to address the needs and particular conditions of workers engaged in digitized hybrid work. It considers that a solution may lie with the extension of the scope and focus of the rights to collective organization and action, and with a re-evaluation of their substantive content. The chapter seeks solutions in worker empowerment through the redeployment of collective labor rights and institutions. The chapter also briefly touches upon illustrative case studies that provide glimpses into possible avenues of traditional and alternative collective action tactics. The relevant current EU framework is used to contextualize the discussion.

This chapter focuses on applying data science and machine learning techniques to real-world problems using R. It covers four main applications: clinical data analysis, social media data collection and analysis, and large-scale data processing.

The chapter begins with exploring clinical data from a dermatology study, demonstrating visual exploration, gradient descent regression, random forest classification, and k-means clustering techniques. It then transitions to social media analysis, specifically working with Reddit APIs to collect and analyze posts, examining relationships between variables like post length, scores, and upvotes.

The YouTube section covers API authentication and data collection for video statistics analysis. Finally, the Yelp analysis demonstrates big data processing techniques, exploring user behavior patterns through correlation analysis, regression modeling, and clustering of review data.

The chapter emphasizes practical API usage, data visualization, statistical testing, and the importance of understanding both the problem and data before analysis.

This chapter examines how the European Union, despite positioning itself as a global leader in combating tax avoidance, has become a central facilitator of corporate tax arbitrage. Through a combination of legal fragmentation, market integration, and judicial rulings favouring corporate mobility, the EU has unintentionally fostered a ‘law market’ enabling multinational corporations (MNCs) to exploit regulatory and tax differentials across member states. The chapter traces how European conduit jurisdictions – particularly the Netherlands, Ireland, Luxembourg, and Switzerland – emerged as key nodes in global tax planning strategies, especially for US-based MNCs. Drawing on evidence from the CORPLINK study and UNCTAD, it shows how these jurisdictions act as hubs for intermediary subsidiaries, structuring global investment chains that reroute value creation, treasury functions, and tax obligations through Europe while bypassing both source and residence countries. Moreover, the chapter highlights how the EU’s internal legal order – especially the subsidiarity principle and European Court of Justice rulings -accelerated the ‘Delaware effect’ of regulatory competition. The paradox is stark: while promoting tax reform and transparency, the European Union has simultaneously entrenched a structural role for European states as gatekeepers of global arbitrage, particularly in how foreign direct investment reaches and exploits developing economies.

This chapter applies the volume’s interactive and holistic approach to the development and analysis of the regulation of remote work. The discussion underscores how employment contracts themselves combine locational elements, of direct significance for remote work, with specifications of the temporal extension of the work contract and its full time or part time nature. Moreover, as the chapter shows, the combinatory constellation of employment situations that results is itself then subject to regulation in ways that the regulatory institutions often frame interactively, using concepts rooted in one type of employment condition to deal with another condition – for example combining features of the contract’s time commitment with its locational definition. The chapter also discusses the importance of the subsidiarity principle for channeling the multilevel and interactive component of remote work’s regulation. These points are then used to address issues of fundamental rights for workers and more specifically, of labor rights under new and evolving employment conditions.

This chapter focuses on using Python for statistical analysis in data science. It begins with statistics essentials, teaching how to calculate descriptive statistics like mean, median, variance, and standard deviation using NumPy. The chapter covers data visualization techniques using Matplotlib to create histograms, bar charts, and scatterplots for exploring data patterns. Key topics include importing data using Pandas DataFrames, performing correlation analysis to measure relationships between variables, and conducting statistical inference through hypothesis testing. Students learn to implement t-tests for comparing means between two groups and ANOVA for comparing multiple groups. The chapter emphasizes practical applications through hands-on examples, from analyzing family age data to comparing exam scores across different classes. These statistical techniques form the foundation for more advanced data science work, enabling students to extract meaningful insights from datasets and make data-driven decisions.

This chapter explores how multinational corporate groups use jurisdictional arbitrage not only to minimize taxation but also to evade liability. Drawing on the concept of CCMCEs, it outlines how corporate planners exploit legal fragmentation to construct liability-resistant structures through techniques such as the Texas two-step, fuse entities, and passive investor emulation. These structures shield parent companies from exposure to tort, regulatory, or criminal liability by disaggregating ownership and control, manipulating corporate registration across jurisdictions, and creating specialized entities designed to absorb legal shocks. The chapter distinguishes between reactive liability strategies, such as dissolving subsidiaries post-litigation, and anticipatory approaches that embed risk insulation directly into the corporate form. Using case studies of Cape Industries, Johnson & Johnson, and the Trump Organization, and data from the CORPLINK project, it illustrates how fuse-like arrangements are deployed to shift liability risk down corporate chains while maintaining control. It argues that liability arbitrage, like tax arbitrage, relies on regulatory gaps between jurisdictions and is often mistaken for mere tax avoidance. Ultimately, such practices erode legal accountability and contribute to a structural ‘race to the bottom’ in corporate regulation.

Remote working – strongly widespread during the covid-19 pandemic –is today one of the main forms of innovation in the world of work. As always, within innovation phenomena we have static elements, from the past, and dynamic elements, looking to change the status quo. Consequently, the evaluation of remote work may be either conservative or innovative. Remote work can be considered as a simple re-proposition of the Fordist-Taylorist Enterprise that does not actually change the characteristics of employment as a not democratic relationship involving the worker submission to the employer managerial, control and disciplinary power. On the other hand, remote work can be recognized as the symptom of a broader cultural, organizational and process change in the firm, allowing the worker to conquer new spaces of freedom and autonomy, which not only allow for a new balance in the relationship between work and life, but also redefine both the factual and juridical connotations of subordination. This chapter analyzes this second perspective and, on the basis of legislation and collective bargaining, tries to define the elements of change in the concept and morphology of subordination within the employment relationship.

This chapter explores a largely overlooked dimension of jurisdictional arbitrage: the manipulation of corporate reporting and disclosure practices at the subsidiary level. While much of the literature focuses on multinational corporations (MNCs) as unified entities, this chapter demonstrates that opacity is often engineered through the dispersed structure of CCMCEs. Drawing on data from the CORPLINK project, it analyses how MNCs exploit jurisdictional inconsistencies in disclosure rules to limit financial transparency, particularly in OFCs. Key strategies include the use of exempted companies, off-balance-sheet entities, disappearing or floating subsidiaries, and shell companies mimicking dormancy. These techniques allow firms to circumvent consolidated reporting obligations, distort public and regulatory perceptions, and obscure the allocation of revenues and profits. Comparative data on independent versus integrated energy firms further illustrates how these practices are unequally distributed and systematically deployed. The chapter calls attention to the inadequacy of current regulatory frameworks and urges international action to standardize disclosure quality and reduce informational blind spots. By revealing how MNCs ‘tell without telling’, the chapter advances our understanding of corporate opacity as a strategic, institutionalized practice deeply embedded in global corporate structuring.

This chapter provides a comprehensive introduction to supervised learning techniques for classification problems. It begins with logistic regression for binary classification, explaining the sigmoid function and gradient ascent optimization. The chapter then covers softmax regression for multi-class problems, followed by k-nearest neighbors (kNN) as an intuitive distance-based classifier.

Decision trees are explored in detail, including entropy, information gain, and the ID3 algorithm, along with derived decision rules and association rules. Random forests are presented as an ensemble method that addresses overfitting by combining multiple decision trees.

The chapter covers Naive Bayes classification based on Bayes’ theorem, despite its "naive" independence assumption. Finally, Support Vector Machines (SVMs) are introduced for both linear and non-linear classification using maximum margin hyperplanes.

Each technique includes hands-on R programming examples with real datasets, practical applications, and exercises to reinforce learning concepts.

This chapter examines how international relations (IR) scholarship has approached two central questions concerning international law and legalisation: why do states create international law, and what makes a particular norm ‘legal’ in nature? It then outlines the concept of legalisation as described in Abbott et al.’s well-known article of the same name. Under the classic legalisation framework, legalisation has three components: obligation, precision and delegation. The chapter argues that the classic OPD framework cannot fully capture the expanding role of non-state actors or conceptualise law as a process. It therefore proposes an adapted model for the transnational legal system that incorporates a crucial omitted dimension – implementation. Implementation refers to the concrete actions taken by agents to translate legal or law-like principles into practical, workable instructions for courts, governments, companies, and other non-state actors.