Analysis of Dynamic Relationship between Changes in Domestic and Overseas Orders and Insolvency of Construction Companies
Bibliographic record
Abstract
본 논문에서는 건설업체 경영상태와 국내외건설사업 구조 간의 동태성을 분석하는 것을 목적으로 한다. 이를 위해 본 연구에서는 건설업체 경영상태를 나타내는 변수로 예상부도확률(EDF)를 활용하였다. 또한 국내외건설사업 구조 변화를 살펴보기 위하여 본 연구에서는 국내건설수주액과 해외건설수주액을 분석변수로 활용하였다. 이들 변수들은 한국상장회사협의회에서 구축한 TS2000, 통계청 및 해외건설협회 자료를 통해 획득하였다. 본 연구의 분석기간은 2001년 1분기부터 2010년 4분기까지로 설정하였다. 분석결과, 국내외 건설시장 진출상황이 양호하게 되면 선험적으로 판단하는 것과 마찬가지로 건설업체 경영상태 역시 양호해지는 것으로 나타났다. 하지만 그 변동정도에는 차이가 발생했다. 또한 건설업체 경영상태가 악화되게 되면 해외건설시장 진출 비중은 높아지는 반면 국내건설시장 진출 비중은 상대적으로 낮아지게 됨을 확인할 수 있다. This study aims to analyze the relationship. The study applies EDF (Expected Default Frequency) as a variable that indicates management status of a construction company. To analyze changes in business structure of construction companies, the study refers to the amounts of domestic and overseas project orders as variables. The data was retrieved from TS2000 established by Korea Listed Companies Association (KLCA), Statistics Korea and International Contractors Association of Korea. The analysis period is between first quarter of 2001 and fourth quarter of 2010. The analysis results showed that as more domestic and overseas orders rolled in for domestic companies, their business conditions improved as the hypothesis suggested. However, the level of improvement varied. Further, when construction companies' business slowed down, the proportion of overseas projects tended to rise, while the ratio of domestic business decreased.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".