Policy Issue Substance and the Revitalization of Legislative Studies

2013 ◽  
Author(s):  
John S. Lapinski
Keyword(s):  
Research Program ◽  
Policy Issue ◽  
Data Sets ◽  
Legislative Studies ◽  
Data Set ◽  
Policy Issues ◽  
Political Preferences ◽  
Vantage Points ◽  
Oriented Approach ◽  
Oriented Research

This chapter seeks to introduce a substance-oriented research program based on policy issues for studying Congress from multiple vantage points. In doing so, it makes serious progress on systematically understanding Theodore Lowi's provocative claim that “policy determines politics,” which, while important, has never been satisfactorily understood, either empirically or theoretically. In advancing a substance-oriented approach to studying policymaking and lawmaking in Congress, the chapter sheds light on several important new tools and ideas to use in determining how policy issue substance matters for lawmaking. These include new data, such as an immense data set on U.S. lawmaking between 1877 to 1994; new and massive measures of political preferences broken down by policy issue areas for U.S. lawmakers spanning the period 1877 to 2010; and fresh approaches to analyzing these new data sets.

scholarly journals Individual development and adaptation: A life-span longitudinal program suited for person-oriented research

2018 ◽  
pp. 63-77
Author(s):  
Lars R. Bergman ◽  
Anna-Karin Andershed ◽  
Anna Meehan ◽  
Henrik Andershed
Keyword(s):  
Life Span ◽  
Longitudinal Studies ◽  
Research Program ◽  
Longitudinal Research ◽  
Individual Development ◽  
Related Factors ◽  
Data Set ◽  
The Rich ◽  
Oriented Approach ◽  
Oriented Research

In this article, we give a presentation of the longitudinal research program Individual Development and Adaptation (IDA) that can be helpful as a template for researchers considering to launch their own longitudinal studies, and that opens the door to IDA for researchers looking for suitable data to be analyzed within their own project or in collab-oration with IDA. We also introduce the holistic-interactionistic theoretical framework of IDA and the associated person-oriented approach – an approach that is especially suited for analyzing the rich IDA data set with its broad coverage of different areas of adjustment and related factors. The paper provides an overview of the essential features of the IDA database, as well as of ongoing and planned IDA research. Keywords: IDA, longitudinal, prospective, person-oriented, development, adaptation

scholarly journals Issues that mobilize Europe. The role of key policy issues for voter turnout in the 2019 European Parliament election

2021 ◽  
pp. 146511652110403
Author(s):  
Daniela Braun ◽  
Constantin Schäfer
Keyword(s):  
Voter Turnout ◽  
Policy Issue ◽  
European Parliament ◽  
Data Sets ◽  
European Elections ◽  
Policy Issues ◽  
Personal Issue ◽  
Key Policy ◽  
High Turnout ◽  
European Parliament Elections

In light of the unexpectedly high turnout in the 2019 European Parliament election, we explore how major transnational policy issues mobilize voters in European electoral contests. Based on the analysis of two data sets, the Eurobarometer post-election survey and the RECONNECT panel survey, we make three important observations. First, European citizens show a higher tendency to participate in European Parliament elections when they attribute greater importance to the issues ‘climate change and environment’, ‘economy and growth’, and ‘immigration’. Second, having a more extreme opinion on the issue of ‘European integration’ increases people's likelihood to vote in European elections. Third, the mobilizing effect of personal issue importance is enhanced by the systemic salience that the respective policy issue has during the election campaign. These findings show the relevance of issue mobilization in European Parliament elections as well as its context-dependent nature.

Political Polarization and Issues: A New Perspective

2013 ◽  
Author(s):  
John S. Lapinski
Keyword(s):  
House Of Representatives ◽  
Policy Issue ◽  
Large Data ◽  
Political Polarization ◽  
Data Set ◽  
Political Preferences ◽  
Issue Area ◽  
Tier 1 ◽  
Long Time ◽  
New Perspective

This chapter discusses how policy issue substance matters for studying political preferences. Fully exploring how policy issue substance matters for studying political polarization in Congress, the chapter begins by introducing a new large data set that comprises the estimated induced preferences of members of the House of Representatives and U.S. senators by policy issue area over a very long time horizon: 1877 to 2010. It also explores the literature on elite polarization in Congress by policy issue area and studies polarization across a 124-year period (1877 to 2010) by the policy issue areas defined as “tier 1.” The chapter shows that issue content is extremely important for understanding political polarization and that many of the empirical facts about polarization depend on not disaggregating policy by issue areas.

scholarly journals With a Little Help From The People? The Role of Public Opinion in Advocacy Success

2017 ◽  
Vol 51 (2) ◽  
pp. 139-164 ◽  
Author(s):  
Anne Rasmussen ◽  
Lars Kai Mäder ◽  
Stefanie Reher
Keyword(s):  
Public Opinion ◽  
News Media ◽  
Policy Issue ◽  
Limited Attention ◽  
Policy Outcomes ◽  
Multilevel Regression ◽  
Media Content Analysis ◽  
Data Set ◽  
Policy Issues

Recent years have witnessed an increased interest in research on advocacy success, but limited attention has been paid to the role of public opinion. We examine how support from the public affects advocacy success, relying on a new original data set containing information on public opinion, advocacy positions, and policy outcomes on 50 policy issues in Denmark, Germany, the Netherlands, Sweden, and the United Kingdom. Claims by advocates are measured through a news media content analysis of a sample of policy issues drawn from national and international public opinion surveys. Our multilevel regression analysis provides evidence that public support affects advocacy success. However, public opinion does not affect preference attainment for some of the lobbying advocates whose influence is feared the most, and the magnitude of its impact is conditional upon the number of advocates who lobby on the policy issue in question.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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