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40 results for “Parliaments”
Gender codification of 412 national (general) and European Parliament elections in six European countries (2003-2021)
<p>This dataset has been produced by applying the Manifesto Gender Analysis (MGA) codebook to 412 national (general) and European Parliament elections in the six countries participating in the UNTWIST project (Denmark, Germany, Hungary, Spain, Switzerland, and the UK) from 2003 to 2021.</p> <p> The Manifesto Gender Analysis coding procedure, developed by WP4 of the UNTWIST consortium, aims to analyse gender-related content in party manifestos. It relies on existing manifestos collected by MARPOR and EM projects from 2003-2021 in six national contexts: Denmark, Germany, Hungary, Spain, Switzerland, and the United Kingdom. The process involves splitting manifestos into quasi-sentences, coding them based on a scheme inspired by previous projects and feminist typology, and completing an expert survey. This method ensures comprehensive analysis and potential scalability through computational methods. </p> <p>The coding procedure involves a series of essential steps, divided in two main activities: the classification of manifestos’ quasi-sentences, and the completion of a survey dedicated to more general concepts which can be gauged by evaluating the content of the entire documents. In the latter case, then, the unit of measure of each coder consists in the manifesto document, whereas in the former the units of measure are quasi-sentences - i.e., arguments denoting a verbal expression of a political idea or issue. Coders are instructed to split sentences containing multiple arguments into quasi-sentences and ensure that each quasi-sentence encapsulates a single political idea or issue. </p> <p>Once the manifestos are split into said units, coders classify the arguments following the MGA coding scheme. The coding scheme (MGA) consists of 5 domains and 25 coding categories, covering various aspects of gender-related issues. Each domain includes an "other" category for relevant statements that do not fit precisely into the defined categories. Apart from coding categories related to specific themes, the coding scheme then includes additional dimensions. The classification process consists of seven steps: (1) assessing whether the quasi-sentence addresses gender-related issues, (2) defining both the domain and coding category, (3) determining whether the quasi-sentence refers to a specific recipient or group based on gender and/or sexual orientation, (4) evaluating intersectionality, (5)<strong> </strong>assigning the sentiment or connotation, (6) determining if it's related to a goal, issue, or policy, and (7) characterising the policy if applicable.</p> <p>After completing the classification of the quasi-sentences in a given manifesto, coders fill in a survey for each manifesto document. The surveys provide information that cannot be directly inferred from the quasi-sentences, focusing on the gender ontology of a manifesto, the degree to which a manifesto entails a binary conception of sexes, the extent to which a manifesto promotes a patriarchal conception of the society, and how much a manifesto promotes heterosexuality as the only normal and socially acceptable sexual orientation of individuals. While the last four characteristics are gauged relying on quasi-interval measures (scales ranging from 0 to 10), the first one, gender ontology, consists in a categorical variable which distinguishes between manifestos with an essentialist ontology – gender and sex are the same and inseparable –, a constructivist ontology – biological sex is mediated through social construction of femininity and masculinity –, and other or undefined ontologies.</p>
UK Parliament Petition Website: Hourly count data
<p>This dataset contains hourly counts of the number of signatures on each petition posted to the UK Parliament petition website from 2015-07-20 to 2016-09-12. The file is gzip compressed text data in tab-separated values format with four columns:</p> <ul> <li>db_id: An internal id number</li> <li>pet_id: The id of the petition on the Parliament website (e.g., 131215 corresponds to the petition https://petition.parliament.uk/archived/petitions/131215 )</li> <li>sigs: The number of signatures observed at datetime.</li> <li>datetime: The date and time of the observation in YYYY-MM-DD HH:mm:SS format (e.g., 2015-07-20 18:24:35)</li> </ul> <p>This data was collected via a Python scrapping script and initially stored in a MySQL database.</p>
Convex inference for community discovery in signed networks (European Parliament Voting Dataset)
<p>This repository contains the necessary tools to reproduce the experiments of the paper</p> <ul> <li>G. Santatmaría, V. Gómez (2015)<br> Convex inference for community discovery in signed networks.<br> NIPS 2015 Workshop: Networks in the Social and Information Sciences</li> </ul> <p>The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here)</p> <p>The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/</p> <ul> <li>data/ : json files with the european votes</li> <li>network.net : signed network built from the votes</li> <li>political_parties.txt : "ground truth" party</li> <li>community_results/ : results for different number of communities and initial vertices</li> <li>dataComputations.py : used to build the signed network</li> <li>dataProcessing.py : used to build the signed network</li> </ul> <p>We would appreciate if you cite the paper after using the data or the code.</p> <p>DEPENDENCIES</p> <p>The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04</p> <p>- For PSL library, you need to have<br> java 1.8<br> you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER'<br> maven 3.x</p> <p>- For libDAI you will need:<br> make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev</p> <p>CODE TO RUN THE FOLLOWING EXPERIMENTS:</p> <p>Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities</p> <p>INSTALL</p> <p>To install the experiments you have to follow the next steps:</p> <p>1 Build the libdai library by doing: make -B on the folder (libdai)</p> <p>2 Generate the class path of the groovy project:<br> mvn clean install<br> mvn dependency:build-classpath-Dmdep.outputFile=classpath.out</p> <p>on the psl root folder (You need to have java 1.8 and maven 3.x installed)</p> <p>3 Grant exec permissions to the run.sh script</p> <p>Options</p> <p>The main python file to run the experiments is</p> <p>evaluatebalanceon_sn.py.</p> <p>It accepts the following parameters:</p> <p>1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less<br> 2 (Int) The number of underlying communities<br> 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45<br> 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default.</p> <p>An example of execution would be:</p> <p>python evaluate_balance_on_sn.py 120 3 0.45 True True</p> <p>The results of the experiments are save in the folder results/<br> Scripts</p> <p>The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy</p> <p>Authors:</p> <p>Guillermo Santamaria & Vicenc Gomez<br> Mar 5, 2017</p> <p>For further questions, please contact vicen.gomez@upf.edu</p>
Questions in European Parliament on Brain Drain
<p>This is data collated for a discourse analysis of the treatment of questions on "brain drain" in the European Parliament. Collated by Jacob A. Hasselbalch for the ENLIGHTEN project (H2020 #649456).</p>
Counting Words That Count: NLP for exploring Romanian Parliament Transcripts
<p>The data is obtained by scraping the cdep.ro website and contains 500k+ instances of speech from the parliament podium from 1996 to 2019. (Up to 2001 only the Chamber of Deputies published transcripts, after jan. 2001 Senate data is also included.) <br> <br> Columns: </p> <p>'index' - incremented integer as row number in order of scraping</p> <p>'title', - title of the scraped page, usually contains the name of the chamber and the exact data</p> <p>'name', - the name of the speaker, preappended with Mr. or Mrs. </p> <p>'speech', - the content of the speech, </p> <p>'gender', - the gender of the speaker</p> <p>'url' - the url to the profile of the speaker (useful for extending the data)</p> <p> </p> <p>CDEPs2.csv - Contains all transcripts, prone to parsing errors. 100% of data.</p> <p>validated-1.csv - Consists of 99% of original data. Less than 1% dropped for convenience. Ready to use.</p>
Excel data collection template on descriptive political representation in national parliaments of the projects Pathways to Power and InclusiveParl adapted for the ActEU project
<p>This file contains the empty data collection template and variable and value labels to code biographical data on legislators for WP4 in the ActEU project. It is an abbreviated version of the codebooks produced by the Pathways to Power project and by the InclusiveParl project.</p>
Aggregate dataset on descriptive representation in the Hellenic Parliament (2019-2023)
<p>This is the dataset provided by the AUTH team on descriptive representation in the Hellenic Parliament for WP4 of the ActEU project. </p>
Aggregate Dataset on Descriptive Representation in the Spanish Parliament (2016-2023)
<p>This is the dataset aggregated at the legislative/party level by the CSIC team from the individual-level data provided by the Sciences Po and CSIC teams on descriptive representation in the Spanish lower chamber of Parliament for WP4 of the ActEU project. </p>
Directory of the European Parliament members
<p>Over the past twenty-five years, a field of research into the careers of Members of European Parliament (MEPs) has developed. Drawing on a massive amount of accessible open data, we have assembled an updated database comprising all MEPs between 1979 and 2025.</p> <p>This dataset contains (some) socio-demographic informations about MEP’s and their carreer paths in EP. Data were scraped from the EP website.</p> <p>Metadata are filled separately in a rich text format (.rtf) document.</p>
Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity (replication data)
<p>Replication dataset for B. de Vet (2023). Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity <em>(In: Political Studies Review)</em></p>
RegioParl Corpus of Parliamentary Debates in Germany's Regional Parliaments (2000-2012)
<p>RegioParl is a linguistically annotated and indexed variant (CWB data format) of a corpus of parliamentary debates in Germany's regional parliaments. The corpus includes all parliamentary protocols of all "Landtage" and the "Bundesrat" between 2000 and early 2012. Some regional parliaments are represented in the corpus with data that predate the year 2000. The corpus preparation resulted from a cooperation project with the Institut für Deutsche Sprache Mannheim (IDS).</p>
MigParl. A Corpus of Speeches on Migration and Integration in Germany's Regional Parliaments
<p>MigParl is an indexed and linguistically annotated corpus of speeches on migration and integration affairs in Germany’s regional parliaments (“Landtage”). The corpus has been prepared in the MigTex Project (principal investigators: Andreas Blätte / University of Duisburg-Essen, Ruud Koopmans / Berlin Social Science Center), using the resources and the infrastructure of the <a href="http://polmine.github.io">PolMine Project</a>.</p> <p>MigTex was part of a larger joint project to establish the research community of the <em>German Centre for Migration and Integration Affairs</em> (<em>Deutsches Zentrum für Migration and Integrationsforschung</em> / DeZIM). Funding awarded by Germany’s <em>Federal Ministry for Family Affairs, Senior Citizens, Women and Youth</em> (<em>Bundesministerium für Familie, Senioren, Frauen und Jugend</em> / BMFSFJ) is gratefully acknowleged.</p>
Aggregate Dataset on Descriptive Representation in the Italian Parliament (2013-2018)
<p>This is the dataset aggregated at the legislature/party level by the CSIC team from the individual-level data provided by the Sciences Po and CSIC teams on descriptive representation in the Italian lower chamber of Parliament for WP4 of the ActEU project. </p>
Lex-Atlas:Covid-19 Parliaments Dataset
<p>Data on the impact on national parliaments resulting from the Covid-19 pandemic mined from country reports published by the Lex-Atlas: Covid-19 project and the Oxford University Press. For more information see https://lexatlas-c19.org</p>
Historic machines from 'prams' to 'Parliament': new avenues for collaborative linguistic research
<p>Recording of presentation of long paper, DH Benelux 2022: RE-MIX. Creation and alteration in DH (Hybrid), 1-3 June 2022.</p> <p>Research in computational linguistics has made successful attempts at modelling word meaning at scale, but much remains to be done to put these computational models to the test of historical scholarship. More importantly, a lot of computational research looks at texts in a historical vacuum, 'synchronically', as linguists would say. <em>Living with Machines</em> is an interdisciplinary research project that rethinks the impact of technology on the lives of ordinary people during the Industrial Revolution. During this project, we decided to address a fundamental question: what did people mean by ‘machine’ and how has this meaning changed over time?</p> <p>This paper outlines how a simple research question like 'what was a machine?' can provide an opportunity to engage the public with our work while also generating data for analysis and new avenues of research in a radically collaborative way.</p>
Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes
<p><strong>Presentation. </strong>For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year.</p> <p>These are the data used in the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” <em>Social Networks</em>, vol. 60, pp. 83–102, 2020. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02082574">hal-02082574</a>⟩</li> </ul> <p><strong>Source code.</strong> The code source is accessible on GitHub: <a href="https://github.com/CompNet/MultiNetVotes">https://github.com/CompNet/MultiNetVotes</a></p> <p><strong>Citation. </strong>If you use these data our this source code, please cite the above paper.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes},</code><br><code> journal = {Social Networks},</code><br><code> year = {2020},</code><br><code> volume = {60},</code><br><code> pages = {83-102},</code><br><code> doi = {10.1016/j.socnet.2019.02.001},</code><br><code>}</code><br><br>----------------------------------------------<br><strong>Details.</strong><br><br><strong># RAW INPUT FILES</strong><br>The 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833<br>The folder structure is as follows:<br>* itsyourparliament/<br>** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.<br>** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)<br>** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs<br><br><strong># ROLLCALL NETWORKS</strong><br>This folder contains two separate zip files regarding rollcall networks:<br>- rollcall-networks: This folder contains only the rollcall networks that are used in the article.<br>- all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data.<br>Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN.<br><br><strong># ROLLCALL PARTITIONS</strong><br>Note that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without any<br>neighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC).<br>The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article.<br><br><strong># ROLLCALL CLUSTERING</strong><br>This folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows:<br>|__ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN.<br>|__ F.purity-k=2-sil=SILHOUETTE_SCORE<br>|__ clu=CLUSTER_NO/<br>|__ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article.<br>|__ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article.<br>----------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
MERICS China Podcast: China and the European Parliament election, with Ivana Karásková and Grzegorz Stec
<p>Ahead of the European Parliament election on June 6-9, 2024, this episode looks at the role of the European Parliament in EU-China relations and the possible impact of the election results on the European “de-risking” agenda among other topics. </p> <p><strong>Johannes Heller-John</strong> talks to <strong>Ivana Karásková</strong> and <strong>Grzegorz Stec</strong>. Ivana is a European China Policy Fellow at MERICS and the founder of MapInfluenCE and China Observers in Central and Eastern Europe (CHOICE) at the Association for International Affairs (AMO) in Prague. Grzegorz is the Head of the MERICS Brussels Office.</p> <p>Recently, Ivana co-authored two reports, one on <a href="https://www.amo.cz/en/foreign-electoral-interference-affecting-eu-democratic-processes/" target="_blank" rel="noopener noreferrer">foreign electoral interference in the EU</a> and one on the <a href="https://www.amo.cz/en/from-the-fringes-to-the-forefront-how-extreme-parties-in-the-european-parliament-can-shape-eu-china-relations/" target="_blank" rel="noopener noreferrer">rise of fringe parties in the EP and their impact on EU-China relations</a>. Grzegorz has published articles on <a href="https://www.merics.org/en/merics-briefs/how-ep-parties-see-china-ev-exports-trade-and-technology-council" target="_blank" rel="noopener">how EP parties see China</a> and on <a href="https://www.merics.org/en/comment/meps-key-lessons-eu-china-policy-during-last-mandate" target="_blank" rel="noopener">key lessons learned by Members of the EP during the last mandate</a>.</p>
Greek Parliament Proceedings 1989-2019
<p>This dataset originated from the work implemented during the course of the Master thesis entitled "<a href="http://www.pyxida.aueb.gr/index.php?op=view_object&object_id=6387">Speech quality and sentiment analysis on the Hellenic Parliament proceedings</a>" at the Athens University of Economics & Business in 2018. It has been updated multiple times since then, in order for the best result to be achieved.</p> <p>It includes 1,194,407 speeches of Greek parliament members with a total volume of 2.15 GB, that where exported from 5,118 parliamentary sitting record files and extend chronologically from 1989 up to 2019.</p> <p>The dataset was created with the three following basic steps:</p> <ul> <li>Collection of the record files of the parliament proceedings that are published on the website of the Greek Parliament <a href="https://www.hellenicparliament.gr/Praktika/Synedriaseis-Olomeleias">https://www.hellenicparliament.gr/Praktika/Synedriaseis-Olomeleias</a></li> <li>Collection of the official published names of all the members of the Hellenic Parliament from the website of the Greek Parliament <a href="https://www.hellenicparliament.gr/Vouleftes/Diatelesantes-Vouleftes-Apo-Ti-Metapolitefsi-Os-Simera/">https://www.hellenicparliament.gr/Vouleftes/Diatelesantes-Vouleftes-Apo-Ti-Metapolitefsi-Os-Simera/</a></li> <li>Detecting speakers and their corresponding speeches from the records and matching the speaker names provided in the records with the official parliament member names.</li> </ul> <p>The dataset consists of a csv file in UTF-8 encoding and includes the following columns of data:</p> <p><strong>member_name:</strong> The official name of the parliament member that talked during a sitting.</p> <p><strong>sitting_date: </strong>The date that the sitting took place. There are cases were more than one sittings took place at the same date.</p> <p><strong>parliamentary_period:</strong> The name and/or number of the parliamentary period that the speech took place in. A parliamentary period includes multiple parliamentary sessions.</p> <p><strong>parliamentary_session: </strong>The name and/or number of the parliamentary session that the speech took place in. A parliamentary session includes multiple parliamentary sittings.</p> <p><strong>parliamentary_sitting: </strong>The name and/or number of the parliamentary sitting that the speech took place in.</p> <p><strong>political_party: </strong>The political party that the speaker belongs to.</p> <p><strong>speaker_info: </strong>Information about the speaker extracted from the text of the proceeding/sitting record that refers to the parliamentary role of the speaker such as Chairman of the Parliament, Finance Minister or similar.</p> <p><strong>speech: </strong>The speech that the member made during the sitting of the Greek Parliament.</p> <p>Any fields that do not include information, due to omissions in the record files, are filled with a NaN value, apart from the speech field, which in such cases remains an empty string.</p> <p>Enjoy!</p>
European Parliament Interpreting Corpus (EPIC)
<p>EPIC v2.0 is a parallel and trilingual (English, Italian, and Spanish) corpus of European Parliament (EP) speeches and their simultaneous interpretations. The data were collected from EP sessions in Feb–Apr, and July 2004, including the speeches of 175 speakers and an unknown number of interpreters. The current version of the EPIC (v2.0) contains 692,585 tokens (source: 247,385, target: 445,200) and 83 h 36 min 14 s of audiovisual recordings (source videos: 27 h 32 min 20 s, target audio: 56 h 3 min 54 s). It is fully transcribed, annotated, and aligned at the recording–transcript level.</p>
Aggregate Dataset on Descriptive Representation in the British Parliament (2017-2024)
<p>This is the dataset aggregated at the legislature/party level by the CSIC team from the individual-level data provided by the Sciences Po and CSIC teams on descriptive representation in the British lower chamber of Parliament for WP4 of the ActEU project. </p>
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