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Examines official and public response to a recent series of bombing, and implications for North African immigrants in France; some focus on suspected terrorists living in Great Britain.

The key aim of this volume is to demonstrate ways in which an understanding of history can be used to inform present-day transport and mobility policies. This is not to say that history repeats itself, or that every contemporary transport dilemma has an historical counterpart: rather, the contributors to this book argue that in many contexts of transport planning a better understanding of the context and consequences of past decisions and processes could lead to more effective policy decisions. Collectively the authors explore the ways in which the methods and approaches of historical research may be applied to contemporary transport and policy issues across a wide range of transport modes and contexts. By linking two bodies of academic research that for the most part remain separate this volume helps to inform current transport and mobility policies and to stimulate innovative new research that links studies of both past and present mobilities.

The 2001 foot and mouth disease (FMD) epidemic cost over £8 billion and wreaked havoc upon the British countryside. The paper examines the institutional response to the crisis and the subsequent inquiries. Drawing on the 'garbage-can model' of organisational choice and ideas of 'policy framing', it argues that the institutional response to FMD was tightly focused on agricultural interests. Subsequently, a compartmentalised approach to lesson learning has been partial in its coverage. The result is that important lessons, of a more holistic and integrated nature, have been overlooked despite the replacement of the Ministry of Agriculture with a new Department for the Environment, Food and Rural Affairs.

Examines several different explanations of policy change based on notions of learning, including notions of political, policy-oriented, lesson-drawing, social, & government learning. These different concepts identify different actors & different effects. Through examination of each approach in terms of who learns what & the effects on subsequent policies, it is found that three distinct types of learning have often been incorrectly juxtaposed. Certain conceptual, theoretical, & methodological difficulties attend any attempt to attribute policy change to policy learning, but this does not detract from the important reorientation of policy analysis that this approach represents. 1 Figure, 44 References. Adapted from the source document.

The Islands (Scotland) Act 2018 was granted Royal Assent in July 2018, introducing a number of measures to underpin the Scottish Government's and the public sector to meet the needs of island communities, now and in the future. One of the provisions of the Act which has not yet come into force relates to Island Community Impact Assessments (ICIAs, or island proofing) as further preparatory work is required. ICIAs require relevant authorities to take into account island communities from the outset (where possible) and how the policy, strategy or service impacts on those communities. This project forms part of the preparatory work on conducting ICIAs. Four early ICIAs were reviewed to explore the information identified and gathered. It also reviewed experiences of rural proofing in England, Northern Ireland, Canada and New Zealand to reflect on lessons that could be learned for future island proofing in Scotland. The report provides recommendations for the future island proofing process in Scotland, including a suggested seven-section ICIA template and associated guidance document (effectively, a seven-stage island proofing process).

What does it mean for a machine learning model to be 'fair', in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise 'fairness' in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.

The last four years have been tough for Australia. We have seen the disastrous 2019-20 fire season, the Covid-19 pandemic, devastating floods and cyclones, the most comprehensive punitive trade measures used against any country in ...