Bibliographic record
Abstract
이 연구는 개인채무자 지원제도를 이용한 과중채무자를 대상으로 그들의 채무 및 인구사회경제적 특성에 따른 유형화를 시도하고, 유형별 특성과 채무자 지원제도 이용 간 연관성을 살펴보았다. 분석은 면접조사를 통해 수집된 일차자료인 개인파산, 개인회생 및 과중채무자의 생활실태와 의식에 관한 데이터를 이용하였으며, 표본은 총 209명의 개인채무자 지원제도 이용자를 포함하였다. 표본을 유형화하기 위해 k-means 군집분석을 활용하였다. 이 과정에서 사례 간 유사성의 분석은 다중대응분석(multiple correspondence analysis)을 이용하였다. 분석결과는 과중채무집단이 크게 노년빈곤형, 장년자영업형, 장년근로형, 청년빈곤형, 장년금융피해형으로 구분될 수 있음을 보여주었다. 노년빈곤형은 개인파산의 이용 비율이 높고 연령대와 경제활동 미참여의 비율도 높아서 경제적 재기의 가능성이 상대적으로 낮았다. 장년자영업형, 장년근로형, 청년빈곤형은 모두 청산형 채무조정제도인 개인파산보다 재건형 제도인 개인회생과 워크아웃 비중이 높았다. 장년금융피해형은 채무 특성이 가장 열악하고 개인파산 이용 비율도 높았다. 개인회생과 워크아웃 제도가 채무조정을 통한 신용회복과 경제적 재기가 주목표인 점을 고려할 때, 청장년층이 이 제도의 주요 이용자인 것은 제도의 취지와 부합하는 현상으로 보인다. 이 연구는 국내에서 처음으로 과중채무자의 유형화를 시도하고, 유형별 특성과 채무자 지원제도 이용 간 연관성을 살펴보았다는데 큰 의의가 있다. This study tested a typology of debt relief program users based on debt and sociodemographic characteristics and examined whether the typology is associated with the use of debt relief programs. The sample was recruited in 2016 from major agencies that provide debt relief programs, including the courts and government-funded credit counseling and recovery service centers. The final sample included a total of 209 people. Analyses were done using multiple correspondence analysis and cluster analysis. The results show that individual debtors can be classified according to their profile type: elderly poor, middle-aged self-employed, middle-aged wage earners, young-adult poor, and middle-aged financial victims. The elderly poor and middle-aged financial victims groups were heavy users of personal bankruptcy (Chapter 7 in the U.S.). On the other hand, the majority of the middle-aged self-employed, middle-aged wage earners, and young-adult poor groups relied on debt restructuring programs, such as Chapter 13 bankruptcy in the U. S. (consumer proposal in Canada). The results suggest that profiles of debt relief program users tend to correspond to the goals of each program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".