Investment arbitrations should not happen too often, because they are costly processes for both parties. Yet they regularly happen. Why? We investigate the hypothesis that investment arbitrations are used as a means of last resort, after dissuasion has failed, and that dissuasion is most likely to fail in situations where significant political risk materializes. Investment arbitration should thus tend to target countries in which highpolitical risk has materialized. In order to test this hypothesis, we focus in this article on two drivers of political risk: bad governance and economic crises. We test various links between these two drivers of risk and arbitration claims. We use an original data-set that includes investment claims filed under the rules of all arbitration institutions as well as ad hoc arbitrations. We find that bad governance, understood as corruption and lack of rule of law (using the WGI Corruption and WGI Rule of Law indexes), has a statistically significant relation with investment arbitration claims, but economic crises do not.

In the process of neoliberal transformation in Turkey, what differentiated the 2000s from the previous two decades were the block sale privatizations of large-scale state enterprises such as PETKİM, Türk Telekom, TÜPRAŞ, and ERDEMİR. These block sales, the conditions of which were shaped by political struggles at different levels, were also constitutive political and ideological moments per se, helping to reproduce a particular perception of social reality at the expense of others. This paper will overview and critically problematize the privatization processes of these four enterprises, all completed under the successive AKP governments in power since 2002. By focusing on the apparently technical and economic aspects of the block-sale processes, such as valuation, efficiency enhancement and marketing, the paper calls into question the increased concerns over their transparency, and wonders whether such concerns can be understood as attempts to mask the substantially corrupt nature of capitalist relations of production, which inescapably makes itself felt during these processes.

There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

This research has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TÜB İTAK (Project No: 118C309). However, the entire responsibility of the publication belongs to the owners of the research. The financial support received from TÜB İTAK does not mean that the content of the publication is approved in a scientific sense by TÜB İTAK.

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