世界経済・社会統計 = World development indicators

Inference and Disputed Authorship: The Federalist

Social data in digital form—including user-generated content, expressed or implicit relations between people, and behavioral traces—are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding “what the world thinks” about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naive usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them.

“For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin1

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.

The Spectacular State explores the production of national identity in post-Soviet Uzbekistan. The main protagonists are the cultural elites involved in the elaboration of new state-sponsored mass-spectacle national holidays: Navro’z (Zoroastrian New Year) and Independence Day. The overall argument is that despite their aspirations to reinvigorate national identity, mass spectacle creators in Uzbekistan have reproduced much of the Soviet cultural production. National identity has been one of the most fraught questions in Central Asia, where nationality was a contradictory and complicated product of the Soviet rule. Although the category of nationality was initiated, produced, and imposed by the Soviet state in the 1920s, it eventually became a source of power and authority for local elites, including cultural producers. The collapse of the Soviet Union opened up possibilities for revising and reversing many understandings manufactured by the socialist regime. Yet, upon her arrival in Tashkent to conduct her research on the renegotiation of national identity in 1995, Laura Adams discovered that instead of embracing newly-found freedom to recover a more authentic history, most Uzbek intellectuals, especially cultural producers working with the state, avoided probing too far in this direction. Rather than entirely discarding the Soviet colonial legacies, they revised their history selectively. Whereas the ideological content of their cultural production shifted from socialism to nationalism, many of the previous cultural ‘‘forms’’ have remained. Similarly, the Uzbek government continued to employ cultural elites to implement the task of reinforcing its nation-building program, thus following the Soviet model of cultural production. The book consists of four chapters. The first chapter delineates the broad themes of national identity building, and the remaining chapters explore mass spectacle creation by distinguishing between three elements: form (Chapter Two), content (Chapter Three), and the mode of production (Chapter Four). The study is based on content analysis of two Olympic Games-style national holidays, interviews with cultural producers, and participation observation of festivals and behind-the-scenes preparation meetings. Although Adams provides a few references to viewers and their attitude toward the public holiday performances, her book does not offer an extended engagement with reception and consumption of these holidays. The comprehensive and multi-layered overview of the process of revising national identity in Uzbekistan is one of the book’s major accomplishments. For Adams, the production of national identity is not a selfevident and seamless production forced by the state but instead a dynamic, complex, and dialogical process of negotiation between various parties (intellectual factions, state officials, mass spectacle producers, etc.). Her account reveals the messy and often contradictory nature of national identity production and thus moves away from the tendency to reify the state and its policies. The book makes a significant contribution to studies of nationalism by suggesting that the production of national identity in Uzbekistan was centrally constituted by the consideration of the ‘‘international audience.’’ Although public holidays, studied by Adams, aimed at fostering national identification, the forms in which these celebrations are performed (including national dances and music) indicate the aspiration of cultural producers to be part of the international community. This kind of national production self-consciously oriented toward the international viewer has been the legacy of the Soviet nationalities policy where all cultural producers had to produce art ‘‘socialist in content, national in form.’’ Notwithstanding the difference in generations or genres,

The Justice Cascade: How Human Rights Prosecutions are Changing World Politics:

The Arab Spring

This package contains functions useful for exploratory data analysis using random forests, which can be fit using the randomForest, randomForestSRC, or party packages (Liaw and Wiener 2002; Ishwaran and Kogalur 2013; Hothorn, Hornik, and Zeileis 2006). These functions can compute the partial dependence of covariates (individually or in combination) on the fitted forests’ predictions, the permutation importance of covariates, as well as the distance between data points according to the fitted model.

/pdf/edarf-exploratory-data-analysis-using-random-forests-1ci1jcltfd.pdf

edarf: Exploratory Data Analysis using Random Forests

The identification of substantively similar policy proposals in both proposed and adopted legislation is important to scholars of public policy diffusion and legislative politics. Conventional, manual, approaches are prohibitively costly in constructing datasets that accurately represent policymaking across policy domains, geographic units, and/or time. We propose the use of text-sequencing algorithms, applied to legislative text, to identify bills that introduce similar policy proposals. We present three ground truth tests, applied to a corpus of 500,000 bills from US-state legislatures. First, we show that bills introduced by ideologically similar sponsors are more likely to exhibit a high degree of text reuse. Second, we show that bills classified by the National Council of State Legislatures as covering the same policies exhibit a high degree of text reuse. Third, we show that rates of text reuse across state borders correlate strongly with the diffusion networks recently introduced by Desmarais, Harden and Boehmke (2015).

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2812607_code2573244.pdf?abstractid=2812607&mirid=1

Text as Policy: Measuring Policy Similarity Through Bill Text Reuse

Despite its rich tradition, there are key limitations to researchers' ability to make generalizable inferences about state policy diffusion. This paper introduces new data and methods to move from empirical analyses of single policies to the analysis of comprehensive populations of policies and rigorously inferred diffusion networks. We have gathered policy adoption data appropriate for estimating policy innovativeness and tracing diffusion ties in a targeted manner (e.g., by policy domain, time period, or policy type) and extended the development of methods necessary to accurately and efficiently infer those ties. Our State Policy Innovation and Diffusion (SPID) database includes 728 different policies coded by topic area. We provide an overview of this new dataset and illustrate two key uses: (1) static and dynamic innovativeness measures and (2) latent diffusion networks that capture common pathways of diffusion between states across policies. The scope of the data allow us to compare patterns in both across policy topic areas. We conclude that these new resources will enable researchers to empirically investigate classes of questions that were difficult or impossible to study previously, but whose roots go back to the origins of the political science policy innovation and diffusion literature.

SPID: A New Database for Inferring Public Policy Innovativeness and Diffusion Networks

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

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Fridolin Linder

Papers

edarf: Exploratory Data Analysis using Random Forests

Text as Policy: Measuring Policy Similarity Through Bill Text Reuse

SPID: A New Database for Inferring Public Policy Innovativeness and Diffusion Networks

Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

Text as Policy: Measuring Policy Similarity through Bill Text Reuse