Can works of art teach us something, perhaps about ourselves, about the world, or about what matters? This chapter addresses whether and how the arts contribute to learning by drawing on insights from epistemology – the branch of philosophy concerned with the nature of knowledge. It considers how the arts contribute to the various ways of knowing that philosophers distinguish: propositional knowledge (also called “know-that”), experiential knowledge, understanding, and skill-based knowledge (also called “know-how”). With respect to each, there are promising ways of working out how art may contribute to our epistemic growth. At the same time, each candidate type of knowledge faces challenges and complications that limit its ability to be the singular answer regarding learning from and through the arts. Ultimately, this chapter suggests that “know-how” – or skill-based knowledge – is an especially fruitful concept for understanding how we become epistemically better off through engagement with the arts. This chapter thus suggests that becoming epistemically better off through engagement with the arts is not about acquiring knowledge of facts, but rather about developing ways of seeing, perceiving, interpreting, and responding to the world more skillfully.

Since the early 2000s, various methods for analysing speech data have emerged. While several publications offer strong overviews of methodological innovations, most of them reflect the state of research from over a decade ago. Recent advances in speech technology and statistics relevant to sociophonetic studies have rarely been addressed. This chapter aims to provide an up-to-date overview of three advanced methods for investigating sociophonetic variation, covering both data preparation and analysis, illustrated with a case study on spoken Scottish English. For data preparation, the chapter shows how OpenAI Whisper, an automatic speech recognition model, can transcribe speech and produce outputs compatible with linguistic software. It also discusses how speech annotation tasks can be improved via LaBB-CAT, a tool for corpus management and data mining. For data analysis, the chapter outlines how sociophonetic studies can benefit from advanced statistical methods, including regression and decision tree modelling as well as model evaluation. A key example is the PrInDT package, developed to optimise conditional inference trees as well as classification and regression tasks.

In their chapter, Roskies, Busch, and Walton present the first phase of a very ambitious project aiming at examination of the causal role of agency in mental disorders. The project is daunting because there are several unknowns. First, there is an issue of making a definition of agency and its variants. Next, it would be necessary to examine those variants across different clinical populations. Finally, there is a need to see whether modulating agency through brain stimulation leads to changes in the psychiatric condition. The authors frame their project within the model of interventionist approach. It is a complex project because this approach requires that all confounding and correlated influences and effects are kept under control. Moreover, it is not clear that the efficacy of deep brain stimulation, which is so successful in the treatment of chronic dystonia, Parkinson’s disease, and some forms of epilepsy, is equally effective in treating mental disorders such as depressive disorder, schizophrenia, or obsessive-compulsive disorder (OCD).

A concise introduction to theoretical description of quantum systems in general, and qubits in particular, in the presence of decoherence (equations of motion for quantum state vectors and density matrices, Lindblad formalism, Bloch vector and Bloch equations).

Over time, United Nations human rights treaty bodies (UNTBs) have developed an admissibility requirement that individuals’ allegations be ‘sufficiently substantiated’ or ‘not manifestly unfounded’. Explanations of these terms have varied, but States, treaty body members and scholars have equated them with a prima facie threshold. Among international tribunals, prima facie is commonly understood to require the complainant to make a plausible claim. However, review of UNTB decisions indicates that application of this requirement clashes with the accepted meaning of prima facie by: (1) often requiring the complainant to present convincing allegations; (2) taking into account – or giving greater weight to – the state’s arguments and evidence at the admissibility stage; and 3) sometimes requiring the complainant to pre-emptively overcome the state’s possible defences. This chapter seeks to identify relevant trends in order to both better understand current UNTB practice and illuminate paths to greater consistency and clarity in admissibility determinations.

This chapter covers control theory. Optimal control balances the performance and robustness of the closed-loop system with the cost of control. This results in an optimization problem constrained by the dynamics. This chapter begins with linear optimal control, including the linear quadratic regulator (LQR) and Kalman filter. These both involve the Riccati equation, which we will derive using Lagrange multipliers. We then explore nonlinear optimal control, deriving the Hamilton-Jacobi-Bellman (HJB) equations. This involves reinforcement learning and dynamic programming, which are optimization frameworks to solve this nonlinear control problem. Next, we develop model predictive control (MPC) as an extension to optimal control for a broader class of control tasks. MPC is widely used in industrial control due to its flexibility, robustness, ease of implementation, and ability to respect constraints. MPC repeatedly solves an optimal control problem on a receding horizon, reinitializing the optimization problem as new sensor information is available. Finally, we discuss linear matrix inequalities, which allow us to reformulate many problems in control as convex optimization problems.

Critical Race Theory provides a lens through which we can examine and unpack the role of rest, self-care and community care in social work spaces. The Rest is Resistance movement, coined by Tricia Hersey of the @napministry encourages us to “disrupt and push back against capitalism and white supremacy by connecting to the liberating power of rest, daydreaming and naps as a foundation for healing and justice.” This chapter provides a counternarrative to traditional notions that burnout can be prevented by individual efforts of social workers and students to better care for themselves, positing that the professionalization of social work, by design, continues to perpetuate inequity and exploit emotional labor.

In his chapter, Ken Kendler set out to explore the prospects of evidential pluralism in a field that admittedly has not been explored in the literature so far: psychiatry. I find it thoroughly impressive how Kendler has engaged with the literature on evidential pluralism, and I fully agree with his words of caution and caveats about mechanisms. In this commentary, I will try to think along with Kendler and offer pointers to other places in this broad area of evidential pluralism that might be of help to those interested in psychiatry.

This chapter explores the optimization behind machine learning. Machine learning uses optimization to tune the parameters of a model to fit data, by minimizing the objective function. Fundamentally, this is an inverse problem solved with optimization. We begin with automatic differentiation and backpropagation, as they are the core algorithmic infrastructure needed to obtain gradients to optimize large-scale machine learning models. Automatic differentiation applies the chain rule to the computational graph of a function to compute its derivative with the same computational complexity and the same order accuracy as evaluating the function itself. This is the idea behind backpropagation in neural network training, although automatic differentiation is used more broadly in inverse problems and control. Next, we show how to train neural networks with optimization. This involves learning rate scheduling, batch normalization, hyperparameter optimization, and vanishing and exploding gradients. We also revisit reinforcement learning from the perspective of deep learning. Finally, we explore how to incorporate physics into machine learning algorithms using techniques from optimization.

Before accessing the UN treaty bodies’ individual communications procedure, a complainant must have exhausted domestic remedies. This admissibility rule exists for good reasons, but it has limits. In particular, exemptions must be recognised in respect to domestic remedies which lack effectiveness, including accessibility. Regrettably, UNTBs are currently reverting to a formalistic and mechanical application of this admissibility rule. What justice requires, however, is the opposite: an expansive consideration of the plethora of barriers that prevent access to domestic justice, as well as a reflection about how each barrier can realistically be evidenced by a complainant. This can be achieved, this chapter argues, through an individual-centred, contextual approach, which achieves the aim of preventing the state from escaping international scrutiny, while highlighting the crucial role domestic justice should play in remedying human rights wrongs.

Chapter 13 extends the use of CERIC to include peer-based discussion and social collaborative annotation (SCA), aiming to enhance critical reading, peer engagement, and interdisciplinary learning. This chapter begins by discussing the unique benefits of SCA as a pedagogical tool, highlighting how it fosters active learning, structured discourse, and collective meaning-making in academic settings. It then provides an in-depth examination of digital tools for SCA, offering guidance on selecting platforms that align with instructional goals, accessibility needs, and institutional infrastructure. This chapter also presents best practices for incorporating CERIC into SCA discussion groups, demonstrating how structured annotation frameworks improve analytical skills, reading comprehension, and peer collaboration. Specific strategies – such as role-based annotation, color-coded highlights, and guided discussions – are outlined to help instructors maximize the impact of SCA on student learning outcomes. Finally, this chapter examines a real-world case study of a four-week doctoral course on interdisciplinary science policy research to illustrate the effectiveness of CERIC + SCA in fostering deeper engagement with primary literature.

The chapter introduces the concept of Earth’s amphibious transformation—the technological and socio-political extension of the human habitat onto sea surfaces since the mid-20th century. It frames this transformation as a key driver of the oceanic Anthropocene, characterized by intensified vertical interactions with spatial layers above and below the sea surface, reaching from fossil fuels beneath the seabed to outer space. Through oil platforms, wind turbines, mariculture cages, offshore rocket launches, and many other types of artificial islands, marine regions have become central to developmentalist agendas and environmental degradation concerns. The chapter establishes two interrelated analytical perspectives: an oceanic-vertical one that reveals new artificial islands’ upward and downward-oriented access to spatial layers, and a terraqueous-horizontal one connecting these artificial islands to coastlines. Ultimately, reorienting our gaze toward the ocean, the chapter proposes a paradigm shift in recognizing the central role of many marine regions in the Anthropocene, emphasizing artificial islands as both symptom and agent of anthropogenic transformations of planetary scale.

Grounded in developmental, social psychological, and education perspectives, this chapter describes how an ethnic–racial identity (ERI) focused intervention can provide an entry point for educators to engage in culturally sustaining pedagogical practices that support the positive academic and psychological adjustment of students and contribute to reducing ethnoracial inequities in the education system. The chapter reviews the theoretical foundation of the Identity Project intervention and the accompanying teacher professional development program that prepares educators to intervene with students on ERI. The Identity Project was initially developed and tested in the United States but has now been tested in multiple European countries. A summary of empirical evidence to date is presented, followed by a more in-depth discussion of challenges related to implementation, evaluation, and scale-up – both logistical challenges and challenges brought about by the sociopolitical context.