scholarly journals Identifying the roots of inequality of opportunity in South Korea by application of algorithmic approaches

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Seungwoo Han
Keyword(s):  
South Korea ◽  
Family Background ◽  
Gradient Boosting ◽  
Inequality Of Opportunity ◽  
Related Factors ◽  
Korean Society ◽  
Light Gradient ◽  
Individual Variables ◽  
Algorithmic Approaches ◽  
Unequal Opportunities

AbstractThis study identifies the roots of inequality of opportunity in South Korea by applying algorithmic approaches to survey data. In contrast to extant studies, we identify the roots of inequality of opportunity by estimating the importance of variables, interpreting the estimated results, and analyzing the importance of individual variables, instead of measuring inequality of opportunity. We apply a decision tree classification algorithm, light gradient boosting machine, and SHapley Additive exPlanations to estimate the importance of the studied variables and interpret the estimated results. According to the estimated results, the region where the individuals grew up, their gender, and their father’s job during their childhood were the main factors contributing to inequality of opportunity. This study proves that the considerable regional disparity and social environment perpetuate gender inequality in South Korean society. It argues that an individual’s socio-economic achievements are strongly influenced by their father’s background, thus, outweighing other family background-related factors. Individuals receive unequal opportunities owing to a combination of region, father’s background, and their own gender, thereby, affecting their socioeconomic achievements. If these factors remain influential from birth to adulthood, removing the conditions that structure them would be one way to achieve equality of opportunity.

scholarly journals Avoidance of Healthcare Utilization in South Korea during the Coronavirus Disease 2019 (COVID-19) Pandemic

2021 ◽  
Vol 18 (8) ◽  
pp. 4363
Author(s):  
Minjung Lee ◽  
Myoungsoon You
Keyword(s):  
General Population ◽  
South Korea ◽  
Healthcare Utilization ◽  
Online Survey ◽  
Negative Impact ◽  
Sociodemographic Characteristics ◽  
Related Factors ◽  
First Case ◽  
Significant Difference ◽  
Health Related

Avoidance of healthcare utilization among the general population during pandemic outbreaks has been observed and it can lead to a negative impact on population health. The object of this study is to examine the influence of socio-demographic and health-related factors on the avoidance of healthcare utilization during the global outbreak of a novel coronavirus (COVID-19) in 2020. Data were collected through an online survey four weeks after the Korea Centers for Disease Control and Prevention (KCDC) confirmed the first case in South Korea; 1000 subjects were included in the analysis. The logit model for regression was used to analyze the associations between sociodemographic and health-related factors regarding the avoidance of healthcare utilization. Among the participants, 73.2% avoided healthcare utilization, and there was no significant difference in the prevalence of healthcare avoidance between groups with (72.0%) and without (74.9%) an underlying disease. Sociodemographic characteristics (e.g., gender, age, income level, and residential area) were related to healthcare avoidance. Among the investigated influencing factors, residential areas highly affected by COVID-19 (i.e., Daegu/Gyeoungbuk region) had the most significant effect on healthcare avoidance. This study found a high prevalence of healthcare avoidance among the general population who under-utilized healthcare resources during the COVID-19 outbreak. However, the results reveal that not all societal groups share the burden of healthcare avoidance equally, with it disproportionately affecting those with certain sociodemographic characteristics. This study can inform healthcare under-utilization patterns during emerging infectious disease outbreaks and provide information to public health emergency management for implementing strategies necessary to improve the preparedness of the healthcare system.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

Inequality of Opportunity in Mexico

2019 ◽  
pp. 134-158
Author(s):  
Roberto Vélez Grajales ◽  
Luis A. Monroy-Gómez-Franco ◽  
Gastón Yalonetzky
Keyword(s):  
Income Inequality ◽  
Lower Bound ◽  
Social Mobility ◽  
Wealth Inequality ◽  
Inequality Of Opportunity ◽  
Unequal Distribution ◽  
Variable Bias ◽  
Unequal Opportunities ◽  
Available Information

Mexico is a country with high levels of inequality and low intergenerational social-mobility rates for those located at the bottom extremes of the wealth distribution. Although such low rates suggest that at least a share of the observed income inequality may be due to an unequal distribution of opportunities, this conjecture has not been thoroughly tested in the literature. The present article fills this gap estimating the lower bound of the contribution of unequal opportunities to income and wealth inequality in Mexico, with an operationalization of the “ex-ante” approach to the measurement of inequality of opportunity. Relying on a national representative survey designed for the analysis of social mobility, namely, the ESRU Survey on Social Mobility in Mexico (2011), we are able to define a broad set of circumstance groups (“types”), encompassing the wealth of the household of origin. This available information reduces the omitted variable bias of previous estimations and allows for a better account of the role of inequality of opportunity in income inequality. Our results show that the lower bound of the contribution of unequal opportunities to total income inequality and total wealth inequality is around 30 per cent, which is substantially higher than previous estimations for Mexico and ranks among the highest values in Latin America.

Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications

2021 ◽  
Author(s):  
Abdul Muqtadir Khan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Injection Rate ◽  
Model Construction ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Fracture Damage ◽  
Boosting Technique

Abstract With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm. For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test. Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models. Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.

Layers of Inequality: Unequal Opportunities and Skin Color in Mexico

2021 ◽  
pp. 003464462110441
Author(s):  
Luis Monroy-Gómez-Franco ◽  
Roberto Vélez-Grajales ◽  
Gastón Yalonetzky
Keyword(s):  
Skin Color ◽  
Wealth Inequality ◽  
Skin Tone ◽  
Inequality Of Opportunity ◽  
Age Cohort ◽  
Parental Wealth ◽  
Unequal Opportunities ◽  
Total Inequality ◽  
Total Wealth ◽  
A Share

We document the contribution of skin color toward quantifying inequality of opportunity over a proxy indicator of wealth. Our Ferreira–Gignoux estimates of inequality of opportunity as a share of total wealth inequality show that once parental wealth is included as a circumstance variable, the share of inequality of opportunity rises above 40%, overall and for every age cohort. By contrast, the contribution of skin tone to total inequality of opportunity remains minor throughout.

Between Institutional Exclusion and Inclusion: Comparing Old and New Chinese Immigrants in South Korea (制度的排斥與包容：在韓國的舊華僑和新華僑之比較)

2017 ◽  
Vol 11 (2) ◽  
pp. 250-277 ◽  
Author(s):  
Claire Seungeun Lee (李承恩)
Keyword(s):  
South Korea ◽  
Chinese Immigrants ◽  
Korean Society ◽  
Republic Of China ◽  
South Korean ◽  
Before And After ◽  
Rise Of China ◽  
Korean Immigration ◽  
The Rise Of China ◽  
Asian Migration

This article explores three Chinese immigrant groups in South Korea. South Korean society characterizes itself with a long-held traditional myth of being a homogenous society. Two waves of migrants from China, however, challenged this myth. The earlier wave took place in the late 19thcentury. The recent, new, wave of Chinese migration took place in the last three decades and coincidently right before and after the normalization of relations between the People’s Republic of China (prc) and South Korea in 1992. Due to the rise of China and the changing dynamics of inter-Asian migration, new migrants from theprcsince the 1990s have changed the demographic composition of foreign citizens in Korea.These new migrants from theprcare mostly ethnic Han (prcChinese), but some are ethnic Korean (Korean Chinese) who holdprccitizenship. Most previous studies have focused on either old (earlier) Chinese immigrants or new (later) Chinese immigrants separately. This paper, in contrast, comparatively investigates these groups utilizing statistics and secondhand source data. This study contends that the mechanisms of institutional exclusion and inclusion in Korean immigration policies, put forward by the policies’ citizenship, legal and economic aspects, produce both new multiculturalism and ethnonationalism. This paper also contends that mechanisms of institutional exclusion and inclusion are a result of the interplay between citizenship and ethnicity.本文對韓國華僑（“舊華僑”）、持中國國籍的中國大陸漢族和朝鮮族（“新華僑”）進行比較。長久以來，在韓國社會裡“單一民族”一直是一個很普遍的傳統現象。但兩波從中國到韓國的華人華僑移民潮卻反駁此現象。早期的移民潮發生在十九世紀末，在此期間移居到韓國的華人一般稱之為韓國華僑（簡稱為“韓華”）。最近這一波新移民潮則是發生在最近30多年，恰好是在發生在中華人民共和國和韓國建交的一九九二年前後。從一九九零年代開始，因中國崛起和亞洲移民的動態變化帶動的中國“新”移民到了韓國，也改變了在韓國社會裡外國剬民的國籍與種族結構。這些來自中國的新移民大部分都是漢族（簡稱為“漢族”），有些則是朝鮮族，這兩個不同的民族都持有中華人民共和國的國籍。已經有許多研究關注移居韓國的華人，但比較不同時代移居至韓國的華人的討論卻非常少見。這個研究便以統計和二手資料為主，特別針對這些在不同時期來到韓國的華人進行比較。本論文分析了在韓國移民政策裡頭制度排斥和包容的機制，筆者分析了這些政策裡的剬民權、法律和經濟等不同層面，發現韓國的一系列移民政策造成了新的多文化主義和民族國家主義。此外，本研究也發現產生制度排斥和包容機制是剬民權和種族性之間的相互作用的結果。 (This article is in English).

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