Party Elite Engagement and Coordination in House Primary Elections: A Test of Theories of Parties

Electoral Systems in Context

The Oxford Handbook of Electoral Systems ◽

10.1093/oxfordhb/9780190258658.013.46 ◽

2017 ◽

pp. 720-740

Author(s):

Steven L. Taylor

Keyword(s):

United States ◽

United States Of America ◽

Electoral Systems ◽

Party Systems ◽

The United States ◽

Primary Elections ◽

Unique Element ◽

Unique System ◽

The World ◽

Elected Office

The United States of America has one of the longest, continually functional electoral systems in the world. On one level, the system is seemingly simple, as it is based predominantly on plurality winners in the context of single-seat districts. However, its extensive usage of primary elections adds a nearly unique element to the process of filling elected office. This mechanism is used more extensively in the United States than in any other case. Additionally, the United States has a complex, and unique, system for electing its national executive. All of these factors help create and reinforce one of the most rigid two-party systems in the world.

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A Harder and Longer Process? Dispelling Myths about Women in Judicial Primary Elections

Justice System Journal ◽

10.1080/0098261x.2021.1902438 ◽

2021 ◽

pp. 1-38

Author(s):

Kate Eugenis

Keyword(s):

Primary Elections

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The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

Social Science Computer Review ◽

10.1177/08944393211006863 ◽

2021 ◽

pp. 089443932110068

Author(s):

Aleksandra Urman ◽

Mykola Makhortykh ◽

Roberto Ulloa

Keyword(s):

Search Engine ◽

Search Engines ◽

Large Scale ◽

Web Search ◽

Primary Elections ◽

Virtual Agents ◽

Search Results ◽

Presidential Primary ◽

Large Scale Analysis ◽

Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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