@newspaperarticle {598216,
	title = {How to conquer partisan gerrymandering},
	journal = {Boston Globe (Op-Ed)},
	volume = {292},
	number = {179},
	year = {2017},
	pages = {A10},
	abstract = {PARTISAN GERRYMANDERING has long been reviled for thwarting the will of the voters. Yet while voters are acting disgusted, the US Supreme Court has only discussed acting {\textemdash} declaring they have the constitutional right to fix the problem, but doing nothing. But as better data and computer algorithms are now making gerrymandering increasingly effective, continuing to sidestep the issue could do permanent damage to American democracy. In\&nbsp;Gill v. Whitford, the soon-to-be-decided challenge to Wisconsin{\textquoteright}s 2011 state Assembly redistricting plan, the court could finally fix the problem for the whole country. Judging from the oral arguments, the key to the case is whether the court endorses the concept of {\textquotedblleft}partisan symmetry,{\textquotedblright} a specific standard for treating political parties equally in allocating legislative seats based on voting.},
	url = {https://www.bostonglobe.com/opinion/2017/12/26/how-conquer-partisan-gerrymandering/MRdsgd3cx7R6MiodBFW5QI/story.html},
	author = {Gary King and Robert X Browning}
}
@article {581966,
	title = {How the news media activate public expression and influence national agendas},
	journal = {Science},
	volume = {358},
	year = {2017},
	pages = {776-780},
	abstract = {
	We demonstrate that exposure to the news media causes Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly{\textemdash}all key components of democratic politics{\textemdash}more often than they would otherwise. After recruiting 48 mostly small media outlets, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimated the causal effect on proximal measures, such as website pageviews and Twitter discussion of the articles{\textquoteright} specific subjects, and distal ones, such as national Twitter conversation in broad policy areas. Our intervention increased discussion in each broad policy area by approximately 62.7\% (relative to a day{\textquoteright}s volume), accounting for 13,166 additional posts over the treatment week, with similar effects across population subgroups.\&nbsp;



	On the\&nbsp;Science website:\&nbsp;Abstract,\&nbsp;Reprint,\&nbsp;Full text, and a comment (by\&nbsp;Matthew Gentzkow)\&nbsp;"Small media, big impact".



	\&nbsp;



	\&nbsp;

},
	url = {http://science.sciencemag.org/cgi/rapidpdf/358/6364/776?ijkey=yJZhbgQUH1Gi.\&keytype=ref\&siteid=sci},
	author = {Gary King and Benjamin Schneer and White, Ariel}
}
@article {401886,
	title = {The Balance-Sample Size Frontier in Matching Methods for Causal Inference},
	journal = {American Journal of Political Science},
	volume = {61},
	number = {2},
	year = {2017},
	month = {2016},
	pages = {473-489},
	abstract = {We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance. To jointly maximize balance and sample size, we introduce the matching frontier, the set of matching solutions with maximum possible balance for each sample size. Rather than iterating, researchers can choose matching solutions from the frontier for analysis in one step. We derive fast algorithms that calculate the matching frontier for several commonly used balance metrics. We demonstrate with analyses of the effect of sex on judging and job training programs that show how the methods we introduce can extract new knowledge from existing data sets.Easy to use, open source, software is available here\&nbsp;to implement all methods in the paper.},
	author = {Gary King and Christopher Lucas and Richard Nielsen}
}
@article {457551,
	title = {booc.io: An Education System with Hierarchical Concept Maps},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {23},
	number = {1},
	year = {2017},
	pages = {571-580},
	abstract = {
	Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present booc.io, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, booc.io is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into booc.io, and perform introductory qualitative evaluation with students.

},
	url = {https://ieeexplore.ieee.org/document/7536150},
	author = {Michail Schwab and Hendrik Strobelt and James Tompkin and Colin Fredericks and Huff, Connor and Dana Higgins and Anton Strezhnev and Mayya Komisarchik and Gary King and Pfister, Hanspeter}
}
@software {674570,
	title = {booc.io: Software for an Education System with Hierarchical Concept Maps},
	year = {2017},
	author = {Michail Schwab and Hendrik Strobelt and James Tompkin and Colin Fredericks and Huff, Connor and Dana Higgins and Anton Strezhnev and Mayya Komisarchik and Gary King and Pfister, Hanspeter}
}
@booklet {558931,
	title = {Brief of Heather K. Gerken, Jonathan N. Katz, Gary King, Larry J. Sabato, and Samuel S.-H. Wang as Amici Curiae in Support of Appellees},
	journal = { Filed with the Supreme Court of the United States in Beverly R. Gill et al. v. William Whitford et al.},
	volume = {16-1161 },
	year = {2017},
	abstract = {SUMMARY OF ARGUMENTPlaintiffs ask this Court to do what it has done many times before. For generations, it has resolved cases involving elections and cases on which elections ride. It has adjudicated controversies that divide the American people and those, like this one, where Americans are largely in agreement. In doing so, the Court has sensibly adhered to its long-standing and circumspect approach: it has announced a workable principle, one that lends itself to a manageable test, while allowing the lower courts to work out the precise contours of that test with time and experience.Partisan symmetry, the principle put forward by the plaintiffs, is just such a workable principle. The standard is highly intuitive, deeply rooted in history, and accepted by virtually all social scientists. Tests for partisan symmetry are reliable, transparent, and easy to calculate without undue reliance on experts or unnecessary judicial intrusion on state redistricting judgments. Under any of these tests, Wisconsin{\textquoteright}s districts cannot withstand constitutional scrutiny.},
	author = {Heather K. Gerken and Jonathan N. Katz and Gary King and Larry J. Sabato and Samuel S.-H. Wang}
}
@article {181441,
	title = {Computer-Assisted Keyword and Document Set Discovery from Unstructured Text},
	journal = {American Journal of Political Science},
	volume = {61},
	number = {4},
	year = {2017},
	month = {2017},
	pages = {971-988},
	abstract = {The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Paradoxically, this often means that the validity of the most sophisticated text analysis methods depend in practice on the inadequate keyword counting or matching methods they are designed to replace. Improved methods of keyword selection would also be valuable in many other areas, such as following conversations that rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look-alike modeling; intelligence analysis; and sentiment and topic analysis. We develop a computer-assisted (as opposed to fully automated) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon Bombings, Chinese social media posts designed to evade censorship, among others.},
	url = {https://doi.org/10.1111/ajps.12291},
	author = {Gary King and Patrick Lam and Margaret Roberts}
}
@article {399886,
	title = {How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument},
	journal = {American Political Science Review},
	volume = {111},
	number = {3},
	year = {2017},
	month = {2017},
	pages = {484-501},
	abstract = {
	The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists and activists, claim that these so-called {\textquoteleft}{\textquoteleft}50c party{\textquoteright}{\textquoteright} posts vociferously argue for the government{\textquoteright}s side in political and policy debates. As we show, this is also true of the vast majority of posts openly accused on social media of being 50c. Yet, almost no systematic empirical evidence exists for this claim, or, more importantly, for the Chinese regime{\textquoteright}s strategic objective in pursuing this activity. In the first large scale empirical analysis of this operation, we show how to identify the secretive authors of these posts, the posts written by them, and their content. We estimate that the government fabricates and posts about 448 million social media comments a year. In contrast to prior claims, we show that the Chinese regime{\textquoteright}s strategy is to avoid arguing with skeptics of the party and the government, and to not even discuss controversial issues. We show that the goal of this massive secretive operation is instead to distract the public and change the subject, as most of the these posts involve cheerleading for China, the revolutionary history of the Communist Party, or other symbols of the regime. We discuss how these results fit with what is known about the Chinese censorship program, and suggest how they may change our broader theoretical understanding of {\textquoteleft}{\textquoteleft}common knowledge{\textquoteright}{\textquoteright} and information control in authoritarian regimes.



	This paper is related to our articles in Science,\&nbsp;{\textquotedblleft}Reverse-Engineering Censorship In China: Randomized Experimentation And Participant Observation{\textquotedblright}, and\&nbsp;the\&nbsp;American Political Science Review,\&nbsp;{\textquotedblleft}How Censorship In China Allows Government Criticism But Silences Collective Expression{\textquotedblright}.

},
	url = {doi:10.1017/S0003055417000144},
	author = {Gary King and Jennifer Pan and Margaret E. Roberts}
}
@booklet {652307,
	title = {OpenScholar},
	year = {2017}
}
@booklet {652306,
	title = {Thresher (acquired by Two Six Technologies, a Carlyle Group Company)},
	year = {2017}
}
@article {161326,
	title = {A Unified Approach to Measurement Error and Missing Data: Details and Extensions},
	journal = {Sociological Methods and Research},
	volume = {46},
	number = {3},
	year = {2017},
	note = {
	This is the second of two articles to appear in the same issue of the same journal by the same authors. \&nbsp;The other one is\&nbsp;{\textquotedblleft}A Unified Approach to Measurement Error and Missing Data: Overview{\textquotedblright}.

},
	pages = {342-369},
	abstract = {
	We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach{\textquoteright}s robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.

},
	url = {http://journals.sagepub.com/doi/full/10.1177/0049124115589052},
	author = {Matthew Blackwell and James Honaker and Gary King}
}
@article {6388,
	title = {A Unified Approach to Measurement Error and Missing Data: Overview and Applications},
	journal = {Sociological Methods and Research},
	volume = {46},
	number = {3},
	year = {2017},
	note = {
	This is the first of two articles to appear in the same issue of the same journal by the same authors. \&nbsp;The second is\&nbsp;{\textquotedblleft}A Unified Approach to Measurement Error and Missing Data: Details and Extensions{\textquotedblright}.

},
	pages = {303-341},
	abstract = {
	Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2017b).

},
	url = {http://journals.sagepub.com/doi/full/10.1177/0049124115585360},
	author = {Matthew Blackwell and James Honaker and Gary King}
}
