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VERSION:2.0
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CALSCALE:GREGORIAN
X-WR-CALNAME:Nate Persily — Lessons from 2024 for the 2026 Election
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260908T063013Z
UID:tag:localist.com\,2008:EventInstance_51207106108034
DTSTART:20251204T200000Z
DTEND:20251204T211500Z
DESCRIPTION:Despite great fears that it would be marred by considerable adm
 inistrative challenges\, the 2024 election was well-run and smooth.  Howev
 er\, controversies and conflict that did not receive attention given the c
 omfortable margin of victory have signaled vulnerabilities for the 2026 el
 ection related to mail-in ballots\, threats to polling places\, and late c
 ounting of ballots. New executive orders and other novel threats to electi
 on administration and the seating of victorious House candidates are creat
 ing confusion as to whether the 2026 election will be run under the same r
 ules as its predecessors.  This talk will canvas the problems of 2024\, th
 e emerging threats of 2025\, and the path for building resilience for the 
 2026 election.\n\nABOUT THE SPEAKER\n\nNate Persily is the James B. McClat
 chy Professor of Law at Stanford Law School and Senior Fellow in the Freem
 an Spogli Institute.  He is co-Director of the Stanford Law AI Initiative 
 and founded the Stanford Cyber Policy Center\, the Program on Democracy an
 d the Internet and the Stanford-MIT Healthy Elections Project.  He served 
 as the Senior Research Director of the Presidential Commission on Election
  Administration and has been appointed by courts on numerous occasions to 
 draw congressional and legislative redistricting plans.  His current work\
 , for which he has been honored as a Guggenheim and Carnegie Fellow\, exam
 ines the impact of social media and artificial intelligence on democracy a
 nd elections.
LOCATION:
SUMMARY:Nate Persily — Lessons from 2024 for the 2026 Election
URL;VALUE=URI:https://events.stanford.edu/event/nate-persily-lessons-from-2
 024-for-the-2026-election
CATEGORIES:Class/Seminar
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