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Yoltar, Çağrı

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Çağrı

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Yoltar

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Yoltar, Çağrı

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2018 - 201932020 - 20225

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    Sacrificial limbs: masculinity, disability and political violence in Turkey
    (Wiley Periodicals, Inc, 2022) N/A; Yoltar, Çağrı; Researcher; N/A; N/A
    The 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 Aciksoz 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, Aciksoz lays bare a dangerously fragile masculinity and its constitutive interactions with state sovereignty, neoliberal governmentality, and ultranationalist politicization.
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    Making the indebted citizen: an inquiry into state benevolence in Turkey
    (Wiley Periodicals, inc, 2020) N/A; Yoltar, Çağrı; Researcher; N/A; N/A
    This 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 Karkeren 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.
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    PublicationOpen Access
    Contentious welfare: the Kurdish conflict and social policy as counterinsurgency in Turkey
    (Wiley, 2020) Department of Sociology; Yörük, Erdem; Yoltar, Çağrı; Faculty Member; Researcher; Department of Sociology; College of Social Sciences and Humanities; 28982; N/A
    The 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.
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    PublicationOpen Access
    Cross-context news corpus for protest event-related knowledge base construction
    (Massachusetts Institute of Technology (MIT) Press, 2021) Department of Sociology; N/A; Department of Computer Engineering; Yörük, Erdem; Hürriyetoğlu, Ali; Gürel, Burak; Duruşan, Fırat; Yoltar, Çağrı; Mutlu, Osman; Yüret, Deniz; Faculty Member; Teaching Faculty; Faculty Member; Researcher; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; 28982; N/A; 219277; N/A; N/A; N/A; 179996
    We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries. The corpus contains document-, sentence-, and token-level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information, constructing knowledge bases that enable comparative social and political science studies. For each news source, the annotation starts with random samples of news articles and continues with samples drawn using active learning. Each batch of samples is annotated by two social and political scientists, adjudicated by an annotation supervisor, and improved by identifying annotation errors semi-automatically. We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting, contributing to the generalizability and robustness of automated text processing systems. This corpus and the reported results will establish a common foundation in automated protest event collection studies, which is currently lacking in the literature.
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    PublicationOpen Access
    Random sampling in corpus design: cross-context generalizability in automated multicountry protest event collection
    (Sage, 2021) Department of Sociology; Yörük, Erdem; Hürriyetoğlu, Ali; Duruşan, Fırat; Yoltar, Çağrı; Faculty Member; Teaching Faculty; Researcher; Department of Sociology; College of Social Sciences and Humanities; 28982; N/A; N/A; N/A
    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.
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    PublicationOpen Access
    A task set proposal for automatic protest information collection across multiple countries
    (Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yoltar, Çağrı; Yüret, Deniz; Gürel, Burak; Duruşan, Fırat; Mutlu, Osman; Teaching Faculty; Faculty Member; Researcher; Faculty Member; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; N/A; 179996; 219277; N/A; N/A
    We propose a coherent set of tasks for protest information collection in the context of generalizable natural language processing. The tasks are news article classification, event sentence detection, and event extraction. Having tools for collecting event information from data produced in multiple countries enables comparative sociology and politics studies. We have annotated news articles in English from a source and a target country in order to be able to measure the performance of the tools developed using data from one country on data from a different country. Our preliminary experiments have shown that the performance of the tools developed using English texts from India drops to a level that are not usable when they are applied on English texts from China. We think our setting addresses the challenge of building generalizable NLP tools that perform well independent of the source of the text and will accelerate progress in line of developing generalizable NLP systems.
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    PublicationOpen Access
    Towards generalizable place name recognition systems: analysis and enhancement of NER systems on English news from India
    (Association for Computing Machinery (ACM), 2018) Department of Sociology; Department of Computer Engineering; Akdemir, Arda; Hürriyetoğlu, Ali; Yörük, Erdem; Gürel, Burak; Yoltar, Çağrı; Yüret, Deniz; Researcher; Teaching Faculty; Faculty Member; Faculty Member; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; N/A; 28982; 219277; N/A; 179996
    Place name recognition is one of the key tasks in Information Extraction. In this paper, we tackle this task in English News from India. We first analyze the results obtained by using available tools and corpora and then train our own models to obtain better results. Most of the previous work done on entity recognition for English makes use of similar corpora for both training and testing. Yet we observe that the performance drops significantly when we test the models on different datasets. For this reason, we have trained various models using combinations of several corpora. Our results show that training models using combinations of several corpora improves the relative performance of these models but still more research on this area is necessary to obtain place name recognizers that generalize to any given dataset.
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    PublicationOpen Access
    Overview of CLEF 2019 lab protestnews: extracting protests from news in a cross-context setting
    (Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yüret, Deniz; Yoltar, Çağrı; Gürel, Burak; Mutlu, Osman; Akdemir, Arda; Teaching Faculty; Faculty Member; Faculty Member; Researcher; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; 179996; N/A; 219277; N/A; N/A
    We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing. The lab consists of document, sentence, and token level information classification and extraction tasks that were referred as task 1, task 2, and task 3 respectively in the scope of this lab. The tasks required the participants to identify protest relevant information from English local news at one or more aforementioned levels in a cross-context setting, which is cross-country in the scope of this lab. The training and development data were collected from India and test data was collected from India and China. The lab attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted results and working notes respectively. We have observed neural networks yield the best results and the performance drops significantly for majority of the submissions in the cross-country setting, which is China.