Securing American elections: how data-driven election monitoring can improve our democracy
The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-drive...
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| Main Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2020
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| Subjects: | |
| Online Access: | BSB01 UBG01 Volltext |
| Summary: | The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-driven method can help to improve confidence in the integrity of American elections |
| Item Description: | Title from publisher's bibliographic system (viewed on 08 Dec 2020) |
| Physical Description: | 1 Online-Ressource (105 Seiten) |
| ISBN: | 9781108887359 |
| DOI: | 10.1017/9781108887359 |
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| author | Alvarez, R. Michael 1964- Adams-Cohen, Nicholas Kim, Seo-young Silvia ca. 20./21. Jh Li, Yimeng ca. 20./21. Jh |
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| isbn | 9781108887359 |
| language | English |
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| spelling | Alvarez, R. Michael 1964- (DE-588)129560324 aut Securing American elections how data-driven election monitoring can improve our democracy R. Michael Alvarez, Nicholas Adams-Cohen, Seo-young Silvia Kim, Yimeng Li Cambridge Cambridge University Press 2020 1 Online-Ressource (105 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 08 Dec 2020) The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-driven method can help to improve confidence in the integrity of American elections Elections / United States / Data processing Adams-Cohen, Nicholas (DE-588)1224986628 aut Kim, Seo-young Silvia ca. 20./21. Jh. (DE-588)1225954444 aut Li, Yimeng ca. 20./21. Jh. (DE-588)122595505X aut Erscheint auch als Druck-Ausgabe 978-1-108-74492-8 https://doi.org/10.1017/9781108887359 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Alvarez, R. Michael 1964- Adams-Cohen, Nicholas Kim, Seo-young Silvia ca. 20./21. Jh Li, Yimeng ca. 20./21. Jh Securing American elections how data-driven election monitoring can improve our democracy Elections / United States / Data processing |
| title | Securing American elections how data-driven election monitoring can improve our democracy |
| title_auth | Securing American elections how data-driven election monitoring can improve our democracy |
| title_exact_search | Securing American elections how data-driven election monitoring can improve our democracy |
| title_exact_search_txtP | Securing American elections how data-driven election monitoring can improve our democracy |
| title_full | Securing American elections how data-driven election monitoring can improve our democracy R. Michael Alvarez, Nicholas Adams-Cohen, Seo-young Silvia Kim, Yimeng Li |
| title_fullStr | Securing American elections how data-driven election monitoring can improve our democracy R. Michael Alvarez, Nicholas Adams-Cohen, Seo-young Silvia Kim, Yimeng Li |
| title_full_unstemmed | Securing American elections how data-driven election monitoring can improve our democracy R. Michael Alvarez, Nicholas Adams-Cohen, Seo-young Silvia Kim, Yimeng Li |
| title_short | Securing American elections |
| title_sort | securing american elections how data driven election monitoring can improve our democracy |
| title_sub | how data-driven election monitoring can improve our democracy |
| topic | Elections / United States / Data processing |
| topic_facet | Elections / United States / Data processing |
| url | https://doi.org/10.1017/9781108887359 |
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