Judges and Their Audiences e-bog
253,01 DKK
(inkl. moms 316,26 DKK)
What motivates judges as decision makers? Political scientist Lawrence Baum offers a new perspective on this crucial question, a perspective based on judges' interest in the approval of audiences important to them. The conventional scholarly wisdom holds that judges on higher courts seek only to make good law, good policy, or both. In these theories, judges are influenced by other people only i...
E-bog
253,01 DKK
Forlag
Princeton University Press
Udgivet
10 januar 2009
Længde
240 sider
Genrer
Politics and government
Sprog
English
Format
epub
Beskyttelse
LCP
ISBN
9781400827541
What motivates judges as decision makers? Political scientist Lawrence Baum offers a new perspective on this crucial question, a perspective based on judges' interest in the approval of audiences important to them. The conventional scholarly wisdom holds that judges on higher courts seek only to make good law, good policy, or both. In these theories, judges are influenced by other people only in limited ways, in consequence of their legal and policy goals. In contrast, Baum argues that the influence of judges' audiences is pervasive. This influence derives from judges' interest in popularity and respect, a motivation central to most people. Judges care about the regard of audiences because they like that regard in itself, not just as a means to other ends. Judges and Their Audiences uses research in social psychology to make the case that audiences shape judges' choices in substantial ways. Drawing on a broad range of scholarship on judicial decision-making and an array of empirical evidence, the book then analyzes the potential and actual impact of several audiences, including the public, other branches of government, court colleagues, the legal profession, and judges' social peers. Engagingly written, this book provides a deeper understanding of key issues concerning judicial behavior on which scholars disagree, identifies aspects of judicial behavior that diverge from the assumptions of existing models, and shows how those models can be strengthened.