Globalization contested: an international political economy of work

This exciting book provides an illuminating account of contemporary globalization that is grounded in actual transformations in the areas of production and the workplace. It reveals the social and political contests that give 'global' its meaning, by examining the contested nature of globalization as it is expressed in the restructuring of work. Rejecting conventional explanations of globalization as a process that automatically leads to transformations in working lives, or as a project that is strategically designed to bring about lean and flexible forms of production, this book advances an understanding of the social practices that constitute global change. Through case studies that span from the labour flexibility debates in Britain and Germany, to the strategies and tactics of corporations and workers, the author examines how globalization is interpreted and experienced in everyday life. Contestation, she argues, is about more than just direct protests and resistances. It has become a central feature of the practices that enable or confound global restructuring. This book offers students and scholars of international political economy, sociology and industrial relations an innovative framework for the analysis of globalisation and the restructuring of work

Neo-assimilationist citizenship and belonging policies in Britain: Meanings for transnational migrants in northern England

The overall aim of this paper is to contribute to debates on the relationships between citizenship and migration in the UK context in the light of recent changes in UK immigration policy. In particular, it focuses on the question of what an increasingly neo-assimilationist state articulation of national belonging means for transnational migrants living in Britain. The paper begins by charting the evolving nature of citizenship conceptualisations in Western neoliberal contexts and illustrates how Britain has responded to this shifting landscape. The context is one of enhanced ‘migration securitization’ wherein the state implies that the integrity of the nation state and its security can only be assured if migration flows and migrants themselves are closely controlled and monitored. This has led to Britain attempting to bolster the formal institution of citizenship (with its attendant rights and responsibilities) and tie it more explicitly to notions of belonging to the nation. Through research with national/regional policy officials and migrant organisations this paper firstly examines the political landscape of citizenship and belonging in Britain as it relates to migrants. Secondly, it draws on research with African transnational migrants in northern England to explore their senses of belonging and ask whether these cohere with the described state discourse or whether their feelings of belonging exist in tension with neo-assimilationist policies designed to promote a core national identity

Introduction: Thinking with Algorithms: Cognition and Computation in the Work of N. Katherine Hayles

In our contemporary moment, when machine learning algorithms are reshaping many aspects of society, the work of N. Katherine Hayles stands as a powerful corpus for understanding what is at stake in a new regime of computation. A renowned literary theorist whose work bridges the humanities and sciences among her many works, Hayles has detailed ways to think about embodiment in an age of virtuality (How We Became Posthuman, 1999), how code as performative practice is located (My Mother Was a Computer, 2005), and the reciprocal relations among human bodies and technics (How We Think, 2012). This special issue follows the 2017 publication of her book Unthought: The Power of the Cognitive Nonconscious, in which Hayles traces the nonconscious cognition of biological life-forms and computational media. The articles in the special issue respond in different ways to Hayles’ oeuvre, mapping the specific contours of computational regimes and developing some of the ‘inflection points’ she advocates in the deep engagement with technical systems

Cloud ethics: algorithms and the attributes of ourselves and others

Louise Amoore examines how machine learning algorithms are transforming the ethics and politics of contemporary society, proposing what she calls cloud ethics as a way to hold algorithms accountable by engaging with the social and technical conditions under which they emerge and operate

Machine learning political orders

A significant set of epistemic and political transformations are taking place as states and societies begin to understand themselves and their problems through the paradigm of deep neural network algorithms. A machine learning political order does not merely change the political technologies of governance, but is itself a reordering of politics, of what the political can be. When algorithmic systems reduce the pluridimensionality of politics to the output of a model, they simultaneously foreclose the potential for other political claims to be made and alternative political projects to be built. More than this foreclosure, a machine learning political order actively profits and learns from the fracturing of communities and the destabilising of democratic rights. The transformation from rules-based algorithms to deep learning models has paralleled the undoing of rules-based social and international orders – from the use of machine learning in the campaigns of the UK EU referendum, to the trialling of algorithmic immigration and welfare systems, and the use of deep learning in the COVID-19 pandemic – with political problems becoming reconfigured as machine learning problems. Machine learning political orders decouple their attributes, features and clusters from underlying social values, no longer tethered to notions of good governance or a good society, but searching instead for the optimal function of abstract representations of data

The deep border

Deep neural network algorithms are becoming intimately involved in the politics of the border, and are themselves bordering devices in that they classify, divide and demarcate boundaries in data. Deep learning involves much more than the deployment of technologies at the border, and is reordering what the border means, how the boundaries of political community can be imagined. Where the biometric border rendered the border mobile through its inscription in the body, the deep border generates the racialized body in novel forms that extend the reach of state violence. The deep border is written through the machine learning models that make the world in their own image – as clusters of attributes and feature spaces from which data examples can be drawn. The ‘depth’ that becomes imaginable in computer science models of the indefinite multiplication of layers in a neural network begins to resonate with state desires for a reach into the attributes of population. The border is spatially reimagined as a set of always possible functions, features, and clusters – as a ‘line of best fit’ where the fraught politics of the border can be condensed and resolved