전쟁난민아동에 대한 국민국가의 환대와 혐오

The nation s hospitality and hatred toward war refugees; Analysis of the semantic network and topic of permission and rejection of incoming refugee movements

본 연구는 공식적으로 한국체류 중인 전쟁난민 가족과 아동을 장기간 지원한 난민전문가 9인에 대한인터뷰를 통하여 난민 유입을 거부하거나 한국내 정착과 적응을 방해하는데 체계적으로 활용되고 있는제도적 문제와 난민인식의 실태를 비정형 빅데이터분석방법인 텍스트마이닝의 의미연결망 및 토픽분석등을 통하여 분석하고자 하였다. 이를 통하여 전쟁난민의 혐오와 환대라는 담론의 틀을 넘어 난민과공동거주할 수 있는 시민사회의 여건을 구성하고자 하였다. 주요 핵심어의 연결중심성을 분석한 결과, 아동-난민인식-가족-한국-공교육-의료보험취업-난민-난민상황-난민신청 등의 순서로 나타났으며, 핵심어의 연결중심성은 TF-IDF분석결과와 다소 상이하였으나 큰 차이는 없었다. 주요 토픽은 3개로 정리되었으며, 1토픽은 난민발생사유와 체류 불인정의 맥락, 2토픽은 체류가 허용된 이후의 곤란상황, 3토픽은 난민이 시민이 될 수 없도록 방어하는 제도로 정리된다. 이러한 분석결과를 바탕으로 논의 및 추후연구를 제언하였다.

This study examines institutional problems and refugee perceptions that are systematically used to deny refugee inflow or to hinder settlement and adaptation in Korea through interviews with nine refugee experts who have provided long-term support for war refugee families and children who are officially living in Korea. The actual situation was analyzed through the semantic network and topic analysis of text mining, which is an unstructured big data analysis method. Through this, it was attempted to create conditions for civil society where refugees and refugees can coexist beyond the framework of discourse of hate and hospitality. As a result of analyzing the connection centrality of key keywords, the following order was found: child - refugee awareness - family - Korea - public education - medical insurance employment - refugee - refugee situation - refugee application. The connection centrality of key words was slightly different from the TF-IDF analysis result, but there was no significant difference. The main topics are organized into 3 topics, topic 1 is the reason for the occurrence of refugees and the context of denial of residence, topic 2 is the difficult situation after being allowed to stay, and topic 3 is the system to protect refugees from becoming citizens. Based on these analysis results, discussion and future research were suggested.