234 research outputs found

    Reimagining Our World at Planetary Scale: The Big Data Future of Our Libraries

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    Carbon Storage Assurance Facility Enterprise (CarbonSAFE): Integrated CCS Pre-Feasibility CarbonSAFE Illinois East Sub-Basin, Final Report

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    U.S. DOE Cooperative Agreement Number: DE-FE0029445Ope

    Can we forecast conflict? A framework for forecasting global human societal behavior using latent narrative indicators

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    The ability to successfully forecast impending societal unrest, from riots and protests to assassinations and coups, would fundamentally transform the ability of nations to proactively address instability around the world, intervening before unrest accelerates to conflict or prepositioning assets to enhance preventive activity. It would also enhance the ability of social scientists to quantitatively study the underpinnings of how and why grievances transition from agitated individuals to population-scale physical unrest. Recognizing this potential, the US government has funded research on “conflict early warning” and conflict forecasting for more than 40 years and current unclassified approaches incorporate nearly every imaginable type of data from telephone call records to traffic signals, tribal and cultural linkages to satellite imagery. Yet, current approaches have yielded poor outcomes: one recent study showed that the top models of civil war onset miss 90% of the cases they supposedly explain. At the same time, emerging work in the economics disciplines is finding that new approaches, especially those based on latent linguistic indicators, can offer significant predictive power of future physical behavior. The information environment around us records not just factual information, but also a rich array of cultural and contextual influences that offer a window into national consciousness. A growing body of literature has shown that measuring the linguistic dimensions of this real–time consciousness can accurately forecast many broad social behaviors, ranging from box office sales to the stock market itself. In fact, the United States intelligence community believes so strongly in the ability of surface-level indicators to forecast future physical unrest more successfully than current approaches, it now has an entire program devoted to such “Open Source Indicators.” Yet, few studies have explored the application of these methods to the forecasting of non-economic human societal behavior and have primarily focused on large-bore events such as militarized disputes, epidemics, and regime change. One of the reasons for this is the lack of high-resolution cross-national longitudinal data on societal conflict equivalent to the daily indicators available in economics research. This dissertation therefore presents a novel framework for evaluating these new classes of latent-based forecasting measures on high-resolution geographically-enriched quantitative databases of human behavior. To demonstrate this framework, an archive of 4.7 million news articles totaling 1.3 billion words, consisting of the entirety of international news coverage from Agence France Presse, the Associated Press, and Xinhua over the last 30 years, is used to construct a database of more than 29 million global events in over 300 categories using the TABARI coding system and CAMEO event taxonomy, resulting the largest event database created in the academic literature. The framework is then applied to examine the hypothesis of latent forecasting as a classification problem, demonstrating the ability of a simple example-based classifier to not only return potentially actionable forecasts from latent discourse indicators, but to quantitatively model the topical traces of the metanarratives that underlie them. The results of this dissertation demonstrate that this new framework provides a powerful new evaluative environment for exploring the emerging class of latent indicators and modeling approaches and that even rudimentary classification-based models may have significant forecasting potential

    An Evaluation of the Carbon Sequestration Potential of the Cambro-Ordovician Strata of the Illinois and Michigan Basins. Final Report

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    U.S. DOE Cooperative Agreement Number DE-FE0002068Ope

    Petrology, Geochronology, and Geophysical Characterization of Mesoproterozoic Rocks in Central Illinois, USA

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    The Precambrian basement rocks of the Eastern Granite-Rhyolite Province (EGRP) in central Illinois (midcontinent region of North America) exhibit a complex history of early volcanism, granite emplacement, and intrusion of mafic rocks. A comprehensive suite of dedicated petrographic analyses, geophysical logs, and drill core from four basement-penetrating wells, two-dimensional and three-dimensional seismic reflection data, and U-Pb age data from the Illinois Basin–Decatur Project (IBDP) and Illinois Carbon Capture Storage (ICCS) Project site provide new constraints for interpreting the Precambrian basement of the Illinois Basin. These new data reveal the basement to be compositionally and structurally complex, having typical EGRP felsic volcanic rocks intruded by the first reported gabbro in the Precambrian basement in Illinois. Zircons (n ¼ 29) from rhyolite give a U-Pb weighted mean average age of 1467 9 Ma. Zircons (n ¼ 3) from a gabbro dike that intrudes the rhyolite yield a concordia age of 1073 12 Ma, which corresponds to Grenville-age extension and represents the first Grenville-age rock in Illinois and in the EGRP. A high-resolution three-dimensional seismic reflection volume, coincident with the four wells, provides a context for interpreting the petrological data and implies a high degree of heterogeneity for basement rocks at the IBDP–ICCS site, as also shown by the drill cores. The occurrence of Grenville-age gabbro is related to a prominent bowl-like structure observed on local two-dimensional seismic reflection profiles and the three-dimensional volume that is interpreted as a deep-seated mafic sill complex. Furthermore, heterogeneities such as the brecciated EGRP rhyolite and later gabbro intrusion observed in the basement lithology at the IBDP–ICCS may reflect previously unknown distal elements of the 1.1 Ga Midcontinent Rift in the EGRP and more likely Grenville-age extension

    State Control and the Effects of Foreign Relations on Bilateral Trade

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    Do states use trade to reward and punish partners? WTO rules and the pressures of globalization restrict states’ capacity to manipulate trade policies, but we argue that governments can link political goals with economic outcomes using less direct avenues of influence over firm behavior. Where governments intervene in markets, politicization of trade is likely to occur. In this paper, we examine one important form of government control: state ownership of firms. Taking China and India as examples, we use bilateral trade data by firm ownership type, as well as measures of bilateral political relations based on diplomatic events and UN voting to estimate the effect of political relations on import and export flows. Our results support the hypothesis that imports controlled by state-owned enterprises (SOEs) exhibit stronger responsiveness to political relations than imports controlled by private enterprises. A more nuanced picture emerges for exports; while India’s exports through SOEs are more responsive to political tensions than its flows through private entities, the opposite is true for China. This research holds broader implications for how we should think about the relationship between political and economic relations going forward, especially as a number of countries with partially state-controlled economies gain strength in the global economy
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