Bibliographic record
Abstract
1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this study is to investigate the effects of various economic contemporary and lagged variables on the frequency of accident of the real estate brokerage in Korea utilizing the time-series model. (2) RESEARCH METHOD This study is focused on empirical analysis using the time series model. The data for this analysis is collected from the Korea Association of Realtors which is the fraternal insurer of real estate brokerage in Korea. (3) RESEARCH FINDINGS We find that the accident frequency of the real estate brokerage in Korea is able to explained by macro economic variables such as economic growth, interest rate, and so on. 2. RESULTS Results obtained in this study can be summarized as follows; 1) Economic depression results in the frequent accidents in fraternal insurance market. However, there is no sign of contemporary effect of economic growth but a quarter lagged effect. 2) There is a positive correlation between the frequency of accidents in fraternal insurance brokerage and one quarter lagged interest rates, but a negative correlation. But the positive correlation between the accidents and interest rates becomes negative about two quarters later. However, the absolute value of the interest effects dwindles over time. 3) The positive contemporary relationship between the price of real estate and the number of accidents is found. 4) Finally, the empirical analysis shows that the number of accidents has been increasing over time even after controlling economic variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.033 |
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".