Suchergebnisse

ABSTRACTThe honorific term gazi has a significant place in right‐wing politics in Turkey as a key symbol of Turkish nationalism and Islamism. Historically a title associated with Muslim warriors and Ottoman and Turkish sovereigns, it has gained a renewed visibility in everyday life and politics since the 1990s, when the Turkish state began to bestow this title on disabled veterans returning from the counterinsurgency war in Kurdistan. As the war's toll rose, thousands of young, lower‐class men who were badly wounded during their mandatory military service ended up joining the ranks of the gazis, and their injured lives and honored status would go on to become an important point of nationalist rhetoric and action. In Sacrificial Limbs, Salih Can Açıksöz takes his readers deep into the world of Turkey's contemporary gazis, chronicling diverse aspects of their lives – from their memories of war and traumatic experiences of injury, to their everyday struggles in the intimacy of their homes, at healthcare institutions, at work, and on the streets. Traversing disabled veterans' social and political networks, Açıksöz lays bare a dangerously fragile masculinity and its constitutive interactions with state sovereignty, neoliberal governmentality, and ultranationalist politicization.

AbstractThis article concerns the making of the indebted citizen in Turkey through state benevolence. It focuses on the materialization of a debt relationship between state and citizen in everyday workings of state‐sponsored welfare programs in the Kurdish region, in the shadow of a protracted armed conflict between the Turkish military forces and the Partiya Karkerên Kurdistan (Kurdistan Workers' Party). In Turkey, as in many other places, welfare benefits are promoted as a state benevolence rather than a citizenship right, and many officials seek to ensure that beneficiaries are credible enough to honor their debts to the state in the form of loyalty and obedience. Examining bureaucratic processes of beneficiary selection, I demonstrate how a dialectic of generous giving and forceful taking congeals in welfare distribution, compelling compliant behavior among the beneficiaries through the power of debt. I argue that what seems to be a free provision by the Turkish state—social assistance—often operates as a mechanism of debt production in practice—another form of political and economic dispossession for the Kurds in Turkey.

AbstractThe period since the 1990s has witnessed the expanding political influence of the Kurdish movement across the country as well as a transformation in the welfare system, manifesting itself mainly in the emergence of extensive social assistance programs. While Turkish social assistance policy has been formally neutral regarding who is entitled to state aid, Kurds have been de facto singled out by these new welfare programs, as is shown by existing quantitative work. Based on a discourse analysis of legislation, parliamentary proceedings, and news media, this article examines the ways in which Turkish governments and policymakers consider the Kurdish question in designing welfare policies. We illustrate that Kurdish mobilization has become a central theme that informed the transformation of Turkish welfare system over the past three decades.

What is the most optimal way of creating a gold standard corpus for training a machine learning system that is designed for automatically collecting protest information in a cross-country context? We show that creating a gold standard corpus for training and testing machine learning models on the basis of randomly chosen news articles from news archives yields better performance than selecting news articles on the basis of keyword filtering, which is the most prevalent method currently used in automated event coding. We advance this new bottom-up approach to ensure generalizability and reliability in cross-country comparative protest event collection from international and local news in different countries, languages, sources and time periods, which entails a large variety of event types, actors, and targets. We present the results of comparing our random-sample approach with keyword filtering. We show that the machine learning algorithms, and particularly state-of-the-art deep learning tools, perform much better when they are trained with the gold standard corpus from a randomly selected set of news articles from China, India, and South Africa. Finally, we also present our approach to overcome the major ethical issues that are intrinsic to protest event coding.