Hostname: page-component-6bf8c574d5-mggfc Total loading time: 0 Render date: 2025-02-17T23:19:59.199Z Has data issue: false hasContentIssue false
  • English
  • Français

Citizen Forecasting: The 2022 French Presidential Election

Published online by Cambridge University Press:  23 March 2022

Yannick Dufresne ,
Bruno Jérôme ,
Michael S. Lewis-Beck ,
Andreas E. Murr Open the ORCID record for Andreas E. Murr [Opens in a new window]  and
Justin Savoie Open the ORCID record for Justin Savoie [Opens in a new window]
Yannick Dufresne
Affiliation:
Université Laval, Canada
Bruno Jérôme
Affiliation:
Université Paris 2 Panthéon-Assas, France
Michael S. Lewis-Beck
Affiliation:
University of Iowa, USA
Andreas E. Murr
Affiliation:
University of Warwick, UK
Justin Savoie
Affiliation:
University of Toronto, Canada1
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Forecasting the 2022 French Presidential Election
Information
PS: Political Science & Politics , Volume 55 , Issue 4 , October 2022 , pp. 730 - 734
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the American Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

1.

The order of authorship of this article is alphabetical to indicate the equal contribution of each author.

References

REFERENCES

Dufresne, Yannick, Jérôme, Bruno, Lewis-Beck, Michael S., Murr, Andreas, and Savoie, Justin. 2022. “Replication Data for ‘Citizen Forecasting: The 2022 French Presidential Election.’” Harvard Dataverse https://doi.org/10.7910/DVN/MUXIKE.CrossRefGoogle Scholar
Graefe, Andreas. 2014. “Accuracy of Vote Expectation Surveys in Forecasting Elections.” Public Opinion Quarterly 78 (S1): 204–32.CrossRefGoogle Scholar
Graefe, Andreas. 2016. “Forecasting Proportional Representation Elections from Nonrepresentative Expectation Surveys.” Electoral Studies 42:222–88.CrossRefGoogle Scholar
Jérôme, Bruno, and Graefe, Andreas. 2022. “Forecasting the 2021 German Federal Election: An Introduction.” PS: Political Science & Politics 55 (1): 61–63.Google Scholar
Jérôme, Bruno, Jérôme, Véronique, and Lewis-Beck, Michael. 1999. “Polls Fail in France: Forecasts of the 1997 Legislative Election.” International Journal of Forecasting 15 (2): 163–74.CrossRefGoogle Scholar
Jérôme, Bruno, and Jérôme-Speziari, Véronique. 2010. “Fonctions de vote et prévisions électorales, une application à la présidentielle française de 2007.” Canadian Journal of Political Science/Revue canadienne de science politique 43 (1): 163–86.CrossRefGoogle Scholar
Lehrer, Roni, Juhl, Sebastian, and Gschwend, Thomas. 2019. “The ‘Wisdom of Crowds’ Design for Sensitive Survey Questions.” Electoral Studies 57:99–109.CrossRefGoogle Scholar
Leiter, Debra, Murr, Andreas, Ramírez, Ericka Rascón, and Stegmaier, Mary. 2018. “Social Networks and Citizen Election Forecasting: The More Friends the Better.” International Journal of Forecasting 34 (2): 235–48.CrossRefGoogle Scholar
Lewis-Beck, Michael S., and Skalaban, Andrew. 1989. “Citizen Forecasting: Can Voters See into the Future?” British Journal of Political Science 19 (1): 146–53.CrossRefGoogle Scholar
Lewis-Beck, Michael S., and Stegmaier, Mary. 2011. “Citizen Forecasting: Can UK Voters See the Future?” Electoral Studies 30 (2): 264–68.CrossRefGoogle Scholar
Lewis-Beck, Michael S., and Tien, Charles. 1999. “Voters as Forecasters: A Micromodel of Election Prediction.” International Journal of Forecasting 15 (2): 175–84.CrossRefGoogle Scholar
Murr, Andreas E. 2011. “‘The Wisdom of Crowds’? A Decentralized Election Forecasting Model That Uses Citizens’ Local Expectations.” Electoral Studies 30 (4): 771–83.CrossRefGoogle Scholar
Murr, Andreas E. 2015. “The Wisdom of Crowds: Applying Condorcet’s Jury Theorem to Forecasting US Presidential Elections.” International Journal of Forecasting 31 (3): 916–29.CrossRefGoogle Scholar
Murr, Andreas E. 2016. “The Wisdom of Crowds: What Do Citizens Forecast for the 2015 British General Election?” Electoral Studies 41 (1): 283–88.CrossRefGoogle Scholar
Murr, Andreas E., and Lewis-Beck, Michael S.. 2022. “Citizen Forecasting of the 2021 German Election.” PS: Political Science & Politics 55 (1): 97–101.Google Scholar
Murr, Andreas E., Stegmaier, Mary, and Lewis-Beck, Michael S.. 2021. “Vote Expectations Versus Vote Intentions: Rival Forecasting Strategies.” British Journal of Political Science 51 (1): 60–67.CrossRefGoogle Scholar
Nadeau, Richard, Bélanger, Eric, and Lewis-Beck, Michael S.. 2012. “Proxy Models for Election Forecasting: The 2012 French Test.” French Politics 10 (1): 1–10.CrossRefGoogle Scholar
Selb, Petec, Göbel, Sascha, and Lachat, Romain. 2020. “How to Poll Runoff Elections.” Public Opinion Quarterly 84 (3): 776–87.CrossRefGoogle Scholar
Temporão, Mickael, Dufresne, Yannick, Savoie, Justin, and van der Linden, Clifton. 2019. “Crowdsourcing the Vote: New Horizons in Citizen Forecasting.” International Journal of Forecasting 35 (1): 1–10.CrossRefGoogle Scholar
van der Linden, Clifton, and Dufresne, Yannick. 2017. “The Curse of Dimensionality in Voting Advice Applications: Reliability and Validity in Algorithm Design.” Journal of Elections, Public Opinion, and Parties 27 (1): 9–30.CrossRefGoogle Scholar
Supplementary material: Link

Dufresne et al. Dataset

Dataset

https://doi.org/10.7910/DVN/MUXIKE
Link
Supplementary material: File

Dufresne et al. supplementary material

Appendix

Download Dufresne et al. supplementary material(File)
File 63.3 KB