Suchergebnisse

This electronic version has been made available under a Creative Commons (BY-NC-ND) open access license. This exciting book provides an illuminating account of contemporary globalisation 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 globalisation as it is expressed in the restructuring of work.Rejecting conventional explanations of globalisation 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 globalisation 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

Dissects the post 9/11 symbiosis between the state and private security firms, and its basis: the conviction that uncertainties-devastation by terrorist attack, financial market collapse-must be discerned and preempted, however unlikely they may be.

Dissects the post 9/11 symbiosis between the state and private security firms, and its basis: the conviction that uncertainties-devastation by terrorist attack, financial market collapse-must be discerned and preempted, however unlikely they may be.

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.

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.

AbstractA 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.

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.

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.

In this article, I explore a specific relation between mathematics and security calculations. Recalling the confrontations between the mathematician Alan Turing and the philosopher Ludwig Wittgenstein in the 1930s, I am interested in the relationship between intuition and ingenuity. During Wittgenstein's 1930 lectures on the foundations of mathematics, Turing interjects in order to insist upon the capacity of number: 'one can make predictions'. Wittgenstein replies that mathematics 'makes no predictions', but instead is a form of grammar: 'taken by itself we shouldn't know what to do with it; it's useless. But there is all kind of use for it as part of a calculus'. It is just such a formulation of a calculus or grammar – 'decision trees', 'event trees', 'attribute-based algorithms' – that characterizes contemporary security. As for Turing, the logic comprises 'two faculties, which we may call intuition and ingenuity'. The intuitive realm of imagination and speculation reaches toward a possible solution, while the ingenuity seeks arrangements of propositions. The advent of 'rules-based' and 'risk-based' security decisions, then, are always already political because they precisely involve combinatorial possibilities whose arrangement has effects in the world.

When US President Barack Obama publicly addressed the data mining and analysis activities of the National Security Agency (NSA), he appealed to a familiar sense of the weighing of the countervailing forces of security and privacy. "The people at the NSA don't have an interest in doing anything other than making sure that where we can prevent a terrorist attack, where we can get information ahead of time, we can carry out that critical task," he stated. "Others may have different ideas," he suggested, about the balance between "the information we can get" and the "encroachments on privacy" that might be incurred (Obama 2013). In many ways, conventional calculations of security weigh the probability and likelihood of a future threat on the basis of information gathered on a distribution of events in the past. Obama's sense of a trading-off of security and privacy shares this sense of a calculation of the tolerance for the gathering of data on past events in order to prevent threats in the future. In fact, though, the very NSA programs he is addressing precisely confound the weighing of probable threat, and the conventions of security and privacy that adhere to strict probabilistic reasoning. The contemporary mining and analysis of data for security purposes invites novel forms of inferential reasoning such that even the least probable elements can be incorporated and acted upon. I have elsewhere described these elements of possible associations, links, and threats as "data derivatives" (Amoore 2011) that are decoupled from underlying values and do not meaningfully belong to an identifiable subject. The analysis of data derivatives for security poses significant difficulties for the idea of a data subject with a recognizable body of rights to privacy, to liberty, and to justice. Adapted from the source document.