Why this work is in the frame
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Bibliographic record
Abstract
In this study, the dynamic relationships between the housing sales market and the Chonsei market use VAR model, and for the spatial range, 16 metropolitan cities·provinces are set, and for the timely range, from the first quarter of 2006 to third quarter of 2014 are set to divide before and after the global financial crisis to perform the empirical analysis. For the macroeconomic variables effecting the housing market, interest rate was set as CD interest rate, the economic growth and housing demand were set as number of economically active people, and finally, the housing supply was set as land transaction volume. As a result of the empirical analysis, the number of economically active people showed to have plus (+) effect and the land transaction volume to have minus (-) effect to the housing sales price in general. When we look at the difference before and after the financial crisis, in the period before the crisis, the Chonsei price and the CD interest rate had minus (-) effect on the housing sales price, but on the other hand, in the period after the crisis, it showed to have a plus (+) effect. This is because in the period before the financial cris, the housing sales market was in the increasing period, and realization of capital gain was possible through sales to have almost no influence of the lease market of housing Chonsei market to the housing sales market, but in the period after the crisis, the housing sales market maintained downward stabilization, but the Chonsei price increase to be judged that the Chonsei market has big influence on the sales market. Regarding influence of CD interest rate on the housing sales market, the reason that it showed plus (+) influence after the financial crisis is judged to be due to the reason that because the government executed continuous decrease in interest rate to activate the housing sales market, but the sales market is maintaining downward stabilization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.018 |
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 it