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Das Neueste aus juristischen Fachzeitschriften

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David Lang, Benjamin Listyg, Brennah V. Ross, Anna V. Musquera, Zeve Sanderson1Polarization and Social Change Lab, 6429Stanford University, Stanford, CA, USA2Department of Psychology, 1355University of Georgia, Athens, GA, USA3Department of Psyc (Journal of Law & Empirical Analysis, 13.01.2026)
Age Verification and Public Adaptation: A Pre-Registered Synthetic Control Multiverse
Albert H. Rivero12358University of Virginia, Charlottesville, VA, USA (Journal of Law & Empirical Analysis, 24.12.2025)
Difference-Splitting Voting: Middle-Ground Votes at the U.S. Supreme Court
(Journal of Law & Empirical Analysis, 23.12.2025)
Corrigendum to “Biased Reputations: Using Cross-Listed Properties to Identify the Negative Effects of Perceived Race on Users’ Reputations on Airbnb”
(Law, Probability and Risk, 16.12.2025)
Distinguishing exogenously and endogenously defined reliability from individual report accuracy in expert and machine evidence
(Journal of Legal Analysis, 26.11.2025)
A new view of formal equality and a case for predistribution
(Journal of Legal Analysis, 24.11.2025)
Remedies in the officer removal cases
(Law, Probability and Risk, 13.11.2025)
Reconciling common-source, specific-source, feature-based, and score-based likelihood ratios
Julian Nyarko, David E. Pozen1Stanford Law School, Legal Innovation Through Frontier Technology Lab, Stanford Institute for Human-Centered AI, Stanford, CA, USA25798Columbia Law School, New York, NY, USA (Journal of Law & Empirical Analysis, 31.10.2025)
Social Media Participation and Scholarly Success in Law
Oisin Suttle1School of Law and Criminology, Maynooth University, Maynooth, Ireland (Journal of Law & Empirical Analysis, 31.10.2025)
Voters do not Distinguish Between International Law Regimes: A Survey Experiment on the Relative Authority of International Law and Courts in the Human Rights and Trade Regimes
(Law, Probability and Risk, 28.10.2025)
The epistemic value of novel predictive success in scientific and criminal investigations: a Bayesian explanation

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