Analyzing constitutional courts as centralizers and the impact of supermajority rules: A dataset on the federalism conflicts on the unconstitutionality of Laws in Mexico.
{"title":"Analyzing constitutional courts as centralizers and the impact of supermajority rules: A dataset on the federalism conflicts on the unconstitutionality of Laws in Mexico.","authors":"Mauro Arturo Rivera León","doi":"10.1016/j.dib.2024.111123","DOIUrl":null,"url":null,"abstract":"<p><p>The paper describes a dataset obtained through the detailed analysis of 688 judgments issued by the Mexican Supreme Court in constitutional controversies related to separation of power disputes within federalism conflicts centering on those involving the constitutionality of legislation. The data was collected in June 2022, after which judgments were extracted from the database of the Mexican Supreme Court and manually classified. With over 9000 data points, the dataset provides information such as the judgment id, the year resolved, the plaintiff, the level of government sued, the presence of the Federal District as a party, the remedy that procedurally could be sought, and the type of normative provision challenged. Furthermore, the dataset provides a time-consuming manual classification of the outcome of all challenged provisions, sorting them as upheld, invalidated, dismissed due to the supermajority requirement to strike down legislation, or dismissed on formal procedural grounds. The dataset could be of potential use to test hypotheses related to the centralizing nature of constitutional courts and other bodies resolving federalism disputes, testing the impact of supermajority rules on courts, and employing data for cross-comparison of unconstitutionality rates. The dataset has also laid a solid foundation for further annotation efforts, which may be undertaken by expanding the coded variables.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111123"},"PeriodicalIF":1.0000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11647168/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2024.111123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The paper describes a dataset obtained through the detailed analysis of 688 judgments issued by the Mexican Supreme Court in constitutional controversies related to separation of power disputes within federalism conflicts centering on those involving the constitutionality of legislation. The data was collected in June 2022, after which judgments were extracted from the database of the Mexican Supreme Court and manually classified. With over 9000 data points, the dataset provides information such as the judgment id, the year resolved, the plaintiff, the level of government sued, the presence of the Federal District as a party, the remedy that procedurally could be sought, and the type of normative provision challenged. Furthermore, the dataset provides a time-consuming manual classification of the outcome of all challenged provisions, sorting them as upheld, invalidated, dismissed due to the supermajority requirement to strike down legislation, or dismissed on formal procedural grounds. The dataset could be of potential use to test hypotheses related to the centralizing nature of constitutional courts and other bodies resolving federalism disputes, testing the impact of supermajority rules on courts, and employing data for cross-comparison of unconstitutionality rates. The dataset has also laid a solid foundation for further annotation efforts, which may be undertaken by expanding the coded variables.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.