Seth Stoughton on the Shooting of Ashli Babbitt
On January 6, a mob of pro-Trump supporters stormed the U.S. Capitol during the certification of the Electoral College vote. As lawmakers were being evacuated by Capitol police, Ashli Babbitt, a 35-year-old Air Force veteran, tried to climb through a shattered window in a barricaded door. Capitol Police Lt. Michael Byrd shot Babbitt as she was climbing through the window and Babbitt died later that day. In the polarized debate over January 6, the death of Ashli Babbitt has become a focal point and one of unusual political valence. Many on the right view her as a martyred hero and the police officer that shot her as an example of excessive force. Those on the left, who have traditionally been outspoken about police killings, have largely stayed quiet. To the extent they've commented, it's been to emphasize the unique circumstances of the Capitol insurrection as justification for the use of lethal force. The Department of Justice, having reviewed the incident, determined that there was insufficient evidence to charge Officer Byrd with violating Babbitt's civil rights, although DOJ did not conclude one way or the other, whether the shooting was justified under the Fourth Amendment.
To work through the legal issues around the shooting of Ashli Babbitt, Alan Rozenshtein spoke with Seth Stoughton, associate professor of law at the University of South Carolina and the coauthor of a recent Lawfare post on the shooting. Stoughton is a nationally recognized expert on police use of force. A former police officer himself, he was a key witness for the murder prosecution of Derek Chauvin, the police officer who killed George Floyd. Alan spoke with Stoughton about the murky factual records surrounding the Babbitt shooting, the complex constitutional and statutory issues that it raises and what its political effects say about the broader prospects for police reform.
Support this show http://supporter.acast.com/lawfare.
Hosted on Acast. See acast.com/privacy for more information.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Repeatable methods, frameworks, mental models, processes, and systems.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A dedicated inventory of concrete resources named in the episode.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.