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Below results based on the criteria 'internet'
Total number of records returned: 3
Research Opportunities - The 2009/10 British Election Study
The 2009/10 British Election Study (BES) will include significant research opportunities for students of voting, elections and public opinion. The BES will have three major components: (a) in-person pre-post election surveys; (b) rolling campaign internet panel survey (RCPS); (c) 48 inter-election monthly continuous monitoring surveys (CMS) with annual panel components. Each CMS survey will offer researchers opportunities to include question batteries including experiments. Participation is free and data release is very fast. Proposals for research modules reviewed by BES Advisory Board and P.I.s. Proposals also entertained for research modules on core and RCPS components.
The Likely Consequences of Internet Voting for Political Representation
Alvarez, R. Michael
civil rights act
In this paper we examine how internet voting might impact political representation. We begin by reviewing the existing academic literature on NVRA and vote-by-mail elections, and then we turn more directly to the internet and electronic elections. First we look carefully at the ``digital divide'' in the United States, using recent survey data. Then we examine the sole existing electoral experiment with internet voting: the 2000 Arizona Democratic presidential primary. We provide evidence indicating that the internet voting experiment in Arizona might have had a negative impact on minority voter rights and political representation. After that, we consider the possible constituencies for internet voting, using polling data from California. We conclude with a summary of our results and our inferences the representational consequences of internet voting.
Extracting Systematic Social Science Meaning from Text
automated content analysis
2008 U.S. Presidential election
We develop two methods of automated content analysis that give approximately unbiased estimates of quantities of theoretical interest to social scientists. With a small sample of documents hand coded into investigator-chosen categories, our methods can give accurate estimates of the proportion of text documents in each category in a larger population. Existing methods successful at maximizing the percent of documents correctly classified allow for the possibility of substantial estimation bias in the category proportions of interest. Our first approach corrects this bias for any existing classifier, with no additional assumptions. Our second method estimates the proportions without the intermediate step of individual document classification, and thereby greatly reduces the required assumptions. For both methods, we also correct statistically, apparently for the first time, for the far less-than-perfect levels of inter-coder reliability that typically characterize human attempts to classify documents, an approach that will normally outperform even population hand coding when that is feasible. We illustrate these methods by tracking the daily opinions of millions of people about candidates for the 2008 presidential nominations in online blogs, data we introduce and make available with this article, and through evaluations in available corpora from other areas, including movie reviews, university web sites, and Enron emails. We also offer easy-to-use software that implements all methods described.