The effects of state coercion on voting outcome in protest movements: a causal forest approach

2021 ◽  
pp. 1-9
Author(s):  
Weiwen Yin ◽  
Weidong Huo ◽  
Danyang Lin
Keyword(s):  
Learning Algorithm ◽  
State Violence ◽  
Economic Insecurity ◽  
Social Unrest ◽  
Protest Movements ◽  
Socioeconomic Characteristics ◽  
District Council ◽  
Tear Gas ◽  
State Coercion ◽  
The Impact

Abstract In this research note, we examine how Hong Kong voters respond to police violence in the recent social movement. We use causal forests, a machine learning algorithm, to estimate the impact of tear gas usage specific to each constituency. Based on the 2019 District Council Election outcome, we find that there is heterogeneity in the effect of state coercion on the vote share of pro-democracy candidates, depending on many socioeconomic characteristics of the constituency. The results imply that economic concerns still matter in the struggle to obtain democracy: citizens who sense economic insecurity in social unrest show less disapproval of state violence.

The Impact of Pre-Election Protests in Electoral Autocracies: The Case of Hong Kong’s Anti-ELAB Movement

2021 ◽  
Vol 57 (03) ◽  
Author(s):  
STAN HOK-WUI WONG ◽  
KELVIN CHUN-MAN CHAN
Keyword(s):  
Hong Kong ◽  
Voter Turnout ◽  
Empirical Support ◽  
Two Dimensions ◽  
Immigrant Status ◽  
Social Cleavages ◽  
Social Unrest ◽  
District Council ◽  
Electoral Impact ◽  
The Impact

Scholars of electoral autocracies accord far more attention to post-election protests than pre-election ones, as the former have the potential to trigger a regime transition. We argue that pre-election protests can have a significant effect on election outcomes. In particular, they are likely to deepen social cleavages along two dimensions: age and immigrant status. The 2019 social unrest in Hong Kong provides a unique opportunity to evaluate the electoral impact of pre-election protests. Comparing public opinion data related to the 2019 and 2015 District Council elections, we find strong empirical support for our argument, as immigrant status and age are strong predictors of voting choices and voter turnout. Our findings imply that exposure to democratic protests may not help in bridging the gap in political attitudes between immigrants and natives.

A Pandemic of Violence? The Impact of COVID-19 on Conflict

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Sara M.T. Polo
Keyword(s):  
Spatial Distribution ◽  
Real Time ◽  
Case Studies ◽  
Armed Conflict ◽  
Event Data ◽  
Social Unrest ◽  
Johns Hopkins University ◽  
The World ◽  
Political Advantage ◽  
The Impact

AbstractThis article examines the impact and repercussions of the COVID-19 pandemic on patterns of armed conflict around the world. It argues that there are two main ways in which the pandemic is likely to fuel, rather than mitigate, conflict and engender further violence in conflict-prone countries: (1) the exacerbating effect of COVID-19 on the underlying root causes of conflict and (2) the exploitation of the crisis by governments and non-state actors who have used the coronavirus to gain political advantage and territorial control. The article uses data collected in real-time by the Armed Conflict Location & Event Data Project (ACLED) and the Johns Hopkins University to illustrate the unfolding and spatial distribution of conflict events before and during the pandemic and combine this with three brief case studies of Afghanistan, Nigeria, and Libya. Descriptive evidence shows how levels of violence have remained unabated or even escalated during the first five months of the pandemic and how COVID-19-related social unrest has spread beyond conflict-affected countries.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

Kindergarten children’s perceptions of the social unrest in Hong Kong

2021 ◽  
pp. 1476718X2110627
Author(s):  
Caroline Cohrssen ◽  
Nirmala Rao ◽  
Puja Kapai ◽  
Priya Goel La Londe
Keyword(s):  
Hong Kong ◽  
Thematic Analysis ◽  
Media Coverage ◽  
Children's Drawings ◽  
Social Unrest ◽  
Protest Movements ◽  
Children's Perceptions ◽  
The Social ◽  
Children’S Drawings ◽  
The World

Hong Kong experienced a period of significant social unrest, marked by protests, from June 2019 to February 2020. Media coverage was pervasive. In July 2020, children aged from 5 to 6 years attending kindergartens in areas both directly and less directly impacted by the protests were asked to draw and talk about what had taken place during the social unrest. Thematic analysis of children’s drawings demonstrates the extent of their awareness and understanding and suggests that children perceived both protestors and police as angry and demonstrating aggression. Many children were critical of police conduct and saw protestors as needing protection from the police. Children around the world have been exposed to protest movements in recent times. The implications for parents, teachers and schools are discussed.

scholarly journals Data-Driven Localization Mappings in Filtering the Monsoon–Hadley Multicloud Convective Flows

2018 ◽  
Vol 146 (4) ◽  
pp. 1197-1218
Author(s):  
Michèle De La Chevrotière ◽  
John Harlim
Keyword(s):  
Learning Algorithm ◽  
Data Driven ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Errors ◽  
Accurate Analysis ◽  
Perfect Model ◽  
Nonlinear Stochastic Model ◽  
Correlation Statistics ◽  
The Impact

This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature measurements is simulated using an idealized radiative transfer model and assimilated to the monsoon–Hadley multicloud model, a nonlinear stochastic model containing several thousands of model coordinates. A serial ensemble Kalman filter is implemented in which the empirical correlation statistics are improved using localization maps obtained from a supervised learning algorithm. The impact of the localization mappings is assessed in perfect-model observing system simulation experiments (OSSEs) as well as in the presence of model errors resulting from the misspecification of key convective closure parameters. In perfect-model OSSEs, the localization mappings that use adjacent correlations to improve the correlation estimated from small ensemble sizes produce robust accurate analysis estimates. In the presence of model error, the filter skills of the localization maps trained on perfect- and imperfect-model data are comparable.

scholarly journals Research on the Design of Government Affairs Platform in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qian Huang ◽  
Xue Wen Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Algorithm ◽  
The Internet ◽  
Chinese Government ◽  
Development Status ◽  
Deep Learning Algorithm ◽  
Government Management ◽  
The Government ◽  
The Impact

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior.

scholarly journals Machine-learning annotation of human splicing branchpoints

2016 ◽  
Author(s):  
Bethany Signal ◽  
Brian S Gloss ◽  
Marcel E Dinger ◽  
Timothy R Mercer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Gene Splicing ◽  
Genetic Encoding ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
A Genome ◽  
Wide Scale ◽  
The Impact ◽  
Splicing Patterns

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.

scholarly journals Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanlin Liu ◽  
Linqiang Yang ◽  
Linchao Li
Keyword(s):  
Machine Learning ◽  
Puerto Rico ◽  
Reference Frame ◽  
Learning Algorithm ◽  
Virgin Islands ◽  
Machine Learning Algorithm ◽  
Climate Factors ◽  
Helmert Transformation ◽  
The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

The Policy Opinions of British Political Activists

1983 ◽  
Vol 31 (4) ◽  
pp. 604-619 ◽  
Author(s):  
Susan Welch ◽  
Donley T. Studlar
Keyword(s):  
United States ◽  
Political Ideology ◽  
The United States ◽  
Election Study ◽  
Political Behaviour ◽  
Socioeconomic Characteristics ◽  
Activist Group ◽  
Policy Attitudes ◽  
Demographic Attributes ◽  
The Impact

This article employs the October 1974 British Election Study to examine the level and nature of political ideology among British political activists, the effects of socioeconomic characteristics on these attitudes, and the impact of the attitudes on political behaviour. On balance, the activist group closely resembles the nonactivist population. Activists are somewhat more ideological in their thinking than nonactivists, but the differences are quite small. Demographic attributes affect the policy attitudes of the élite slightly more than the nonactivists, but again differences are small. The influence of issue attitudes on voting is about the same for activists and nonactivists. These results stand in contrast to studies showing large élite-mass policy differences in the United States and other work documenting ideological orientations in higher levels of the Labour Party.

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