Gestão de documentos no Poder Legislativo: análise do funcionamento de uma Comissão Parlamentar de Inquérito

Estudo de caráter exploratório que visa analisar o funcionamento de uma Comissão Parlamentar de Inquérito da Câmara dos Deputados a partir do ferramental metodológico da gestão de documentos e da arquivologia. A pesquisa foi realizada a partir das fontes primárias do acervo da Comissão Parlamentar de Inquérito da Pirataria de Produtos Industrializados. As unidades da Câmara dos Deputados objeto de análise foram a Coordenação de Arquivo e o Departamento de Comissões. A interpretação dos dados e a discussão teórica apoiaram-se na literatura especializada nas áreas de gestão de documentos, arquivologia, ciência da informação. A implantação da gestão de documentos foi analisada tendo em conta a sua relevância para o alcance da transparência administrativa. Foram analisados os tipos de controle realizados pelos parlamentos e abordadas as principais características das Comissões Parlamentares de Inquérito. Os resultados indicaram que: i) a gestão de documentos é uma importante ferramenta de gerenciamento para as modernas administrações; ii) a aplicação da gestão de documentos nas Comissões Parlamentares de Inquérito pode contribuir para maior eficácia desses instrumentos legislativos de investigação; iii) a implantação de um programa de gestão de documentos possibilita maior acesso do público aos documentos e às informações da instituição.

El nuevo consenso de Augusto

[ES] En el caso romano, la stásis por antonomasia es la crisis de la República, cuya resolución traumática conduce a la superación del propio sistema republicano y al surgimiento del Principado de Augusto. El régimen augusteo, tras una fase previa de enfrentamientos civiles, se consolida, apoyado, entres otros elementos, en un nuevo consenso y en la reelaboración de la tradición republicana anterior, que permiten difuminar los elementos autocráticos del sistema. La idea de patria constituye un concepto clave en la ideología del nuevo régimen.

User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages)