The Globalization of Natural Resources: How External Actors Affect Political Survival in Resource Rich Countries

This dissertation examines the effect of external actors, including foreign investors, the home governments of foreign investors, and international organizations: IOs), on leadership survival in resource rich countries. According to the existing literature, resource rich countries care less about external reputation and have a higher level of political risks for foreign investors, so, theoretically, they would tend to nationalize the resource sectors, especially in the presence of resource nationalism. In reality, however, resource rich countries cooperate closely with foreign actors and join IOs that constrain themselves. This dissertation provides a theory to explain this puzzle, by modeling the interaction among foreign actors, the leaders of resource rich countries, and the domestic opposition. It argues that leaders of resource rich countries tend to maintain a close friendship with powerful foreign countries, not only because resource rich countries have strategic or economic importance, but also because they by nature face a higher level of revolutionary threat. By providing support to leaders who are friends of theirs, which depresses the opposition, foreign actors help the leaders of resource rich countries to stay in power. An empirical analysis on oil ownership and leadership turnover shows that a leader is more likely to survive when the oil is foreign owned. There are several foreign policy tools that foreign actors can use to assist the leaders, including military intervention, foreign aid, and support from IOs. Membership in IOs also has a similar effect on leadership survival, because IOs can legitimize the leaders and cover their unpopular activities

Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098