Kemal Tahir'in dünyası

Taha Toros Arşivi, Dosya No: 133-Kemal Tahir ve Yorgun SavaşçıUnutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010

Social Identity and Collective Action : Two Case Studies of Unionization in the Turkish Information Technologies (IT) Sector

Information Technology (IT) workers are widely considered as incompatible with the practices and goals of unionization. Unionism in the IT sector has become even more difficult where socio-economic conditions and union legislation constitute another obstacle to development, as with the case of Turkey. A primary motivation of this study has been the gap in the research dedicated to the critical evaluation of the relationship between collective action and worker attitudes in the specific Turkish IT context. This study focuses on two contrasting cases of unionization in the Turkish IT sector; one of which was successful (UNIBEL), the other unsuccessful (IBM Turk). This study primarily explores answers to two questions: 1) How did IBM Turk and UNIBEL workers engage in collective actions?; and 2) How did the specific individual, group-based and contextual dynamics play a role in the different unionization outcomes of these two cases? This study argues that IT workers are likely to unionize when being and acting as a union member is consistent with their individual and group identity, and when they fit with the social environment of unionism. The social identity perspective provides an incorporated approach by considering an interaction between workers, their groups, cognitions and their social contexts. A variety of data is based on comparative case study and collected with in-depth, semi-structured and group interviews with IT workers, union experts and academicians. In general, the research findings demonstrated the significant effect of workerâ s identification with a social group on their decision to or not to engage in collective action. The research findings also indicated the importance of social and political contextual factors in both cases. Overall, group dynamics and social contextual factors significantly led to negative perceptions and decreased the likelihood that IT workers would participate in union action

Effects of The Maxillary Molar Distalization with Zygoma-Gear Appliance on Maxillary Third Molars

Aim:The aim of the present study was to evaluate the effects of a newly produced appliance, named as Zygoma-Gear Appliance (ZGA), on maxillary third molars. We also investigated the effects of ZGA on skeletal, dentoalveolar, and soft tissue measurements.Subjects and Methods:The study group included 10 patients (mean age: 18.3±1.8 years) treated with ZGA system supported with zygomatic anchorage miniplates. The changes due to the distalization were evaluated from the lateral cephalometric films taken before and after distalization by means of Paired t-test.Results:The mean treatment period to achieve a Class I molar relationship was 7.9±3.1 months. The distalization amount of the maxillary molar teeth was 3.8±2.1 mm (p 0.05).Conclusions:Maxillary molar distalization without anchorage loss was achieved with ZGA. Maxillary third molars were slight affected by the distalization force

Soziale Identität und Kollektivmaßnahmen : Zwei Fallstudien der gewerkschaftliche Organisierung in den türkischen Informationstechnologien (IT) Branche

Information Technology (IT) workers are widely considered as incompatible with the practices and goals of unionization. Unionism in the IT sector has become even more difficult where socio-economic conditions and union legislation constitute another obstacle to development, as with the case of Turkey. A primary motivation of this study has been the gap in the research dedicated to the critical evaluation of the relationship between collective action and worker attitudes in the specific Turkish IT context. This study focuses on two contrasting cases of unionization in the Turkish IT sector; one of which was successful (UNIBEL), the other unsuccessful (IBM Turk). This study primarily explores answers to two questions: 1) How did IBM Turk and UNIBEL workers engage in collective actions?; and 2) How did the specific individual, group-based and contextual dynamics play a role in the different unionization outcomes of these two cases? This study argues that IT workers are likely to unionize when being and acting as a union member is consistent with their individual and group identity, and when they fit with the social environment of unionism. The social identity perspective provides an incorporated approach by considering an interaction between workers, their groups, cognitions and their social contexts. A variety of data is based on comparative case study and collected with in-depth, semi-structured and group interviews with IT workers, union experts and academicians. In general, the research findings demonstrated the significant effect of worker s identification with a social group on their decision to or not to engage in collective action. The research findings also indicated the importance of social and political contextual factors in both cases. Overall, group dynamics and social contextual factors significantly led to negative perceptions and decreased the likelihood that IT workers would participate in union action

Harvest index, biomass production and their relationships with tuber yield in potatoes

Ten potato cultivars (Mârfona, Ditta, Ar850019, Armundo, 300/86, Diamant, Bolesta, Helena, Signal and Agria) randomly sampled among the 43 entries in a micro yield trial were used in the study and the tuber yield, the biomass and the harvest index were determined.Simple correlation regression and multiple regression analyses were applied to the data. Significant corretEjjion and regression coefficients determined between tuber yield and biomass and the vegetative parts of the plant above ground indicated that the selection of potato genotypes with high biomass and vegetative parts above ground may increase tuber yield.Ten potato cultivars (Mârfona, Ditta, Ar850019, Armundo, 300/86, Diamant, Bolesta, Helena, Signal and Agria) randomly sampled among the 43 entries in a micro yield trial were used in the study and the tuber yield, the biomass and the harvest index were determined.Simple correlation regression and multiple regression analyses were applied to the data. Significant corretEjjion and regression coefficients determined between tuber yield and biomass and the vegetative parts of the plant above ground indicated that the selection of potato genotypes with high biomass and vegetative parts above ground may increase tuber yield

The use of quantitative eeg for differentiating frontotemporal dementia from late-onset bipolar disorder

WOS: 000430198600004PubMed ID: 29284291The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies