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The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora.
If active labour market policies are to mean more than disciplining and nudging claiming working-age benefits, the questions of how and under what circumstances employers are more or less willing to hire non-employed benefit claimants of working age, and how this is affected by claimants’ ascribed social characteristics, are crucial ones. Over the last decade a growing body of work has provided fruitful insights into employer preferences and behaviour with respect to engagement in employment services (Ingold and Stuart, 2015; Ingold and Valizade, 2017; Bredgaard, 2018). This chapter considers and compares employer engagement in national-level active labour market programmes (ALMP) in the UK and Germany through examination of a selection of government-commissioned research into public programmes intended to integrate a variety of claimant groups (long-term and short-term unemployed; lone parents; sick and disabled people; older jobseekers; newly arrived refugees) into the labour market. The chapter is organized as follows. The first section provides a brief overview of the organization of the public employment service (PES) and changing patterns of employment participation during the last 40 years. The second section considers employers’ views of benefit claimants and the PES. The third section outlines the types of policy tools used to activate claimants into the labour market and foster employer engagement. The fourth section explores the integrative capacity of employers, namely whether they are willing and/or able to recruit benefit claimants and/or participate in the services offered by the PES to facilitate this.
Labour market trends and employment service Institutions
Over the last 40 years the size of the labour force aged 16–64 in employment in the UK has expanded considerably, rising from a low point of around 23.2 million in 1983 to 31.6 million by the end of 2019 (ONS, 2021b). The overall growth in employment has been mirrored by an increase in the employment rate for people aged 16–64, albeit subject to fluctuation with recessionary periods and their aftermath. Prior to the emergence of the COVID-19 pandemic in 2020/ 21, the UK economy had experienced three recessions during this period – 1980–81; 1990–91 and 2008–09.
The principle of maximum entropy is a well-known approach to produce a model for data-generating distributions. In this approach, if partial knowledge about the distribution is available in terms of a set of information constraints, then the model that maximizes entropy under these constraints is used for the inference. In this paper, we propose a new three-parameter lifetime distribution using the maximum entropy principle under the constraints on the mean and a general index. We then present some statistical properties of the new distribution, including hazard rate function, quantile function, moments, characterization, and stochastic ordering. We use the maximum likelihood estimation technique to estimate the model parameters. A Monte Carlo study is carried out to evaluate the performance of the estimation method. In order to illustrate the usefulness of the proposed model, we fit the model to three real data sets and compare its relative performance with respect to the beta generalized Weibull family.
There is a growing interest in the role of employers in the delivery and success of activation policies. In this chapter, we put forward a typology of policy approaches to employer engagement in activation policies. We identify three main policy approaches: regulation, facilitation and negotiation. The policy approaches rely on different assumed problems and different target groups of unemployed and apply different types of governance and policy instruments. The typology that we put forward can be used to classify national policy approaches in the emerging field of employer engagement in activation policies.
The chapter begins with a discussion of the growing attention for employer engagement in activation policies and research. In the second section, we describe the typology of policy approaches to employer engagement. The third section illustrates the typology by analysing and comparing recent developments in activation policies aimed at strengthening the role of employers in the UK, Denmark and The Netherlands. We conclude with a discussion of our contribution to debates about the role of employers in activation policies.
Activation policies and employer engagement
In more than three decades of active welfare state and labour market reforms, and a vast associated scholarly literature, the role of employers has received surprisingly little attention in policies, debates and research. Since the early 1990s, when the diffusion of active labour market policies accelerated across a range of countries, activation policies have mainly focused on the supply side of the labour market. Their objectives have been to motivate unemployed people through supportive and disciplinary measures to actively look for jobs, develop their skills and competences and be more flexible with respect to the kinds of jobs they are willing to accept (Dingeldey, 2007; Bonoli, 2013; Ingold and Stuart, 2015; Bredgaard, 2018).
Similarly, activation researchers have mainly focused on the supply side. Scholars have been preoccupied with classifying and categorizing different types of (supply-side) activation policies, such as workfare versus enabling regimes (Dingeldey, 2007); labour market attachment versus human capital approaches (Lødemel and Trickey, 2000); and liberal versus universal types (Barbier and Ludwig-Mayerhofer, 2004).
For many years the labour market model in the UK was bound up with a predominant concern with job quantity, but with considerably less attention to job quality (Lauder, 1999). Recently however, there has been a shift in policymaking towards a greater concern with the idea of ‘good work’. In active labour market policy (ALMP) the shift is most readily seen in a growing interest in labour market progression, and a process of policy searching for how employment services might support greater labour market mobility.
Within this context, this chapter explores ALMP approaches and practice in the UK alongside a wider national discourse about increasing good work. The chapter evaluates the extent to which ALMP is a weak link in seeking progress towards good work: firstly, as a result of the historically embedded nature of the employer engagement function within particular types of networks of employers with basic labour demand needs; and secondly how this has been supported by a work-first system in which jobseekers are encouraged, and can be mandated, to accept available opportunities. The argument is made that within this system there is only limited scope for public employment services to engage with a good work agenda, or to exert upward institutional pressure on job quality. However, the current context of labour and skills shortages offers ALMP an opportunity to capitalize on some upward pressures on job quality.
The chapter is structured as follows. The labour market context in the UK is described initially, followed by an appraisal of recent developments around the good work agenda and a discussion of the labour market trends which frame current opportunities. The following sections then provide a discussion of the evolution of ALMP in the UK, and the role of employer engagement within ALMP set against a changing policy context: but one in which work-first remains largely embedded. The final section provides a discussion of what this evidence suggests about the relationship between ALMP and job quality.
Context
Historically, the focus of employment policy in the UK has been on reducing unemployment. Hence, the quantity of jobs available has been a primary concern.
We propose a multilingual data-driven method for generating reading comprehension questions using dependency trees. Our method provides a strong, deterministic and inexpensive-to-train baseline for less-resourced languages. While a language-specific corpus is still required, its size is nowhere near those required by modern neural question generation (QG) architectures. Our method surpasses QG baselines previously reported in the literature in terms of automatic evaluation metrics and shows a good performance in terms of human evaluation.
Under the influence of γ-quanta (60Co, P = 9.276 rad/s, T = 300 K), the amount, formation rate, and radiation-chemical yield of molecular hydrogen obtained from the radiolysis process that changes the mass of water (m = 0.0001 ÷ 0.8 g) have been defined in the created nano-SiO2/H2O system with m = 0.2 g mass and d = 20 nm particle size. It was determined that the radiation-chemical yield of molecular hydrogen obtained from the water radiolysis process in the nano-SiO2/H2O system created by the adsorption of water on the nanoparticle surface had a low value. In systems created with the addition of water, the radiation-chemical yield of molecular hydrogen obtained from its radiolysis increased in direct proportion to the water mass. This proves that due to ionizing rays, the yield of electrons emitted from the nanoparticle surface into the water and solvated there increases. Therefore, the radiation-chemical yield of molecular hydrogen is higher than that of the adsorbed system.
The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.
There exist two notions of equivalence of behavior between states of a Labelled Markov Process (LMP): state bisimilarity and event bisimilarity. The first one can be considered as an appropriate generalization to continuous spaces of Larsen and Skou’s probabilistic bisimilarity, whereas the second one is characterized by a natural logic. C. Zhou expressed state bisimilarity as the greatest fixed point of an operator $\mathcal {O}$, and thus introduced an ordinal measure of the discrepancy between it and event bisimilarity. We call this ordinal the Zhou ordinal of $\mathbb {S}$, $\mathfrak {Z}(\mathbb {S})$. When $\mathfrak {Z}(\mathbb {S})=0$, $\mathbb {S}$ satisfies the Hennessy–Milner property. The second author proved the existence of an LMP $\mathbb {S}$ with $\mathfrak {Z}(\mathbb {S}) \geq 1$ and Zhou showed that there are LMPs having an infinite Zhou ordinal. In this paper we show that there are LMPs $\mathbb {S}$ over separable metrizable spaces having arbitrary large countable $\mathfrak {Z}(\mathbb {S})$ and that it is consistent with the axioms of $\mathit {ZFC}$ that there is such a process with an uncountable Zhou ordinal.
A wide range of disciplines are directing their methods and tools to help address the challenges of healthcare. Chief among these are design and operational research (OR). Though they have much in common, these two disciplines have existed in isolation for most of their history and there is currently a gulf between the two research communities. In this position paper, we rapidly review the contributions of design and OR in healthcare. We then identify similarities and complementarities between the two disciplines and communities when they consider healthcare systems. Finally, we propose practical steps to enable better collaboration. Our focus is on finding ways in which the two disciplines complement each other. When applying design to healthcare services, designers may wish to learn from OR, which has a long history of supporting improvements in healthcare organisation and services, particularly using quantitative data and analysis and modelling methods. In return, design has distinctive qualities that could augment the OR approach, such as its emphasis on wide and creative search for potential solutions, and iterative co-production and prototyping of solutions with clients. Better collaboration will require a coordinated effort but could yield a more comprehensive and effective approach to improving healthcare systems.
We formulate and explore two basic axiomatic systems of type-free subjective probability. One of them explicates a notion of finitely additive probability. The other explicates a concept of infinitely additive probability. It is argued that the first of these systems is a suitable background theory for formally investigating controversial principles about type-free subjective probability.
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Premiering at Perth Festival in 2020, Hecate is the first stage adaptation of Shakespearean work, in this case Macbeth, to be performed entirely in one Aboriginal language from Australia, specifically the Noongar language from Western Australia’s southwest. Australia is home to hundreds of Aboriginal languages, most of which are endangered due to settler-colonial suppression of Aboriginal culture. Today, although there are over 30,000 Noongar people, the Noongar language is rarely heard spoken in full sentences. More than being a significant artistic achievement, presenting Shakespeare in Noongar has provided a rare opportunity for Noongar and other people to actively engage with the Noongar language in deep and lasting ways. As a nation with a noted cultural cringe, Australia places high cultural value on Shakespeare. The opportunity to develop Hecate as a Noongar-language work arose because engaging with the English literary tradition – and particularly Shakespeare – attracted the necessary government and philanthropic support, media attention and audience interest. In Hecate, Shakespeare’s venerated status has been subversively used as a chink in the settler-colonial armour through which Noongar cultural activism, and deeper ‘felt’ intercultural understanding has been achieved via various collaborative processes, most importantly in developing a Noongar language-speaking ensemble of Noongar actors.
Shakespeare education is being reimagined around the world. This book delves into the important role of collaborative projects in this extraordinary transformation. Over twenty innovative Shakespeare partnerships from the United Kingdom, the United States, Australia, New Zealand, the Middle East, Europe and South America are critically explored by their leaders and participants. Structured into thematic sections covering engagement with schools, universities, the public, the digital and performance, this book offers vivid insights into what it means to teach, learn and experience Shakespeare in collaboration with others. Diversity, equality, identity, incarceration, disability, community and culture are key factors in these initiatives, which together reveal how complex and humane Shakespeare education can be. Whether you are interested in practice or theory, this collection showcases an abundance of rich, inspiring and informative perspectives on Shakespeare education in our contemporary world.
This chapter describes an innovative collaborative teaching model developed by colleagues Paul Prescott (Warwick), Fiona Gregory (Monash) and Gabriel García Ochoa (Monash) using what is known as ‘the international portal space’, a state-of-the-art teleconferencing system developed by the University of Warwick, England and Monash University, Australia. This technology is fundamentally different to platforms such as Zoom and Microsoft Teams, the use of which became widespread during the COVID-19 global pandemic of 2020. Unlike Zoom, portal pedagogy is predicated on in-person learning that it combines with digital technology to allow students from different institutions to work together in real time (in our case, early morning in the United Kingdom and evening in Australia) and in real spaces on a shared syllabus. In the unit ‘Local and Global Shakespeares’, offered in 2016 and 2017, students on opposite sides of the globe were able to engage with Shakespeare, building their own and a shared knowledge of Shakespearean performance in local and global frameworks. Working alongside students from a different cultural context also forced students to reconsider their understanding of the ‘natural’ and ‘given’ in relation to Shakespeare, and thus, in relation to their understanding of culture more broadly. This chapter examines the application of portal pedagogy and other strategies that we employed to show how this unit sought to reimagine the possibilities of internationally collaborative Shakespearean teaching and learning.