• Korean Journal of Culture and Social Issue
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• v.22 no.4
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• pp.567-593
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• 2016

This study examined longitudinal mediated effects of informal labeling on the relationship between adolescent abuse and academic achievement using autoregressive cross-lagged modeling. Data were obtained from the second, third, and fourth waves of the middle school student cohort (N=3,168) of the Korean Youth Panel Survey. The major longitudinal findings of this study are as follows: First, adolescent abuse was found to have a positive association with future informal labeling. Second, informal labeling was found to have a negative association with future academic achievement. Finally, the longitudinal relationship between adolescent abuse and academic achievement was partially mediated by informal labeling. Based on these results, this study suggests directions for adolescent abuse prevention. The need for education and prevention of informal labeling was discussed, as well as the direction of intervention programs for adolescents with experience of informal labeling. Furthermore, this study may provide empirical evidence for labeling theory and contribute to increasing awareness on the longitudinal influence of adolescent abuse and informal labeling.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.299-317
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• 2008

The school drop-out among the youth has grown to become a serious social problem since about 2000 and calls for an attention to its seriousness. Therefore, this study reviewed the statitistical reports and the previous empirical findings on the school drop-out and integrated to establish a comprehensive understanding of this social phenomenon. The main concepts and terminologies on school drop-out, the current statistics, the possible causal factors and the usual trajectory the youth take after dropping-out of school were discussed to conceptualize the issue. Analyses indicated 12 characteristics of the students who dropped out of school. Those 12 characteristics were restructured according to the ecological conceptual model. The social instability and the financial crisis in the 1990's has eroded the stability of the primary environments of adolescents such as family and school. The family breakdowns from divorce and other reasons weakened psychological and financial support for adolescents. The diminished authority of teachers and school over students exposed conflicts between teacher and students, students' loss of purpose and interest in academic attainment. The adolescents showed emotional reponses like increased level of depression, helplessness, aggression, indicated cognitive reponses such as the loss of purpose and interest in studying, a heightened sense of uncertainty of the future, and behavioral responses like sexual acting out behaviors, and bullying. The unmet psychological needs of adolescents result in run-away and school drop-out behaviors, which in turn progress into juvenile delinquency as the society fails to provide adequate and appropriate guidance and interventions. The intervention strategies at the national level were proposed and the limitations of the study were discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.