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Negative References to Amicus Briefs in Judicial Reasoning

Published online by Cambridge University Press:  17 March 2025

Johan Lindholm
Affiliation:
Department of Law, Umeå University, Umeå, Sweden
Daniel Naurin*
Affiliation:
Department of Political Science, University of Oslo, Oslo, Norway
Philipp Schroeder
Affiliation:
Department of Political Science, Ludwig-Maximilians-University Munich, Munich, Germany
*
Corresponding author: Daniel Naurin; Email: daniel.naurin@stv.uio.no
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Abstract

We argue that negative references to amicus curiae briefs in high court judgments – instances where a court explicitly signals disagreement with the legal arguments in such briefs – are a significant and understudied feature of judicial reasoning. We theorize that such references may provide courts with a tool for increasing the precision of its case law, fostering its legitimacy, and increasing compliance pressure. Our empirical analysis of the Court of Justice of the European Union indicates that negative references are used both to boost its legitimacy and to specify not only what the law is, but what it is not.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of the Law and Courts Organized Section of the American Political Science Association
Figure 0

Figure 1. Path diagram for path analyses in Models 3 and 4. The predictors’ variances and covariance are given by $ {\phi}_{11},{\phi}_{22} $ and $ {\phi}_{12} $. Note: The residuals for the endogenous predictor $ {y}_1 $ and the outcome $ {y}_2 $ are given by $ {\zeta}_1 $ and $ {\zeta}_2 $, with their respective variances given by $ {\psi}_1 $ and $ {\psi}_2 $.

Figure 1

Figure 2. Regression coefficient estimates with 95% confidence intervals for path analyses in Model 1 (N = 3,224) and Model 2 (N = 3,224). Notes: Both models include year and national area of law fixed effects. The top panel plots coefficient estimates for predictors predicting CJEU decision, the bottom panel plots coefficient estimates for predictors predicting Negative reference.

Figure 2

Figure 3. Logistic regression coefficient estimates with 95% confidence intervals for Model 3 (N = 2,305), sub-setting for cases after Nice Treaty reforms. Notes: Model 3 includes year and national area of law fixed effects. Coefficients’ standard errors are clustered by case proceeding.

Figure 3

Table 1. Regression Coefficients for ML Logistic Regression Models at the Submission Level

Figure 4

Figure 4. Regression coefficient estimates with 95% confidence intervals for path analyses in Model 6 (N = 6,881). Notes: Units of analysis are individual Member State observations. Model 6 includes year and national area of law fixed effects. The top panel plots coefficient estimates for predictors predicting CJEU decision, the bottom panel plots coefficient estimates for predictors predicting Negative reference.

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