Bibliographic record
Abstract
This paper measures the degree of capital account openness and financial stability index, and uses GDP to represent economic development. Recent research mainly focuses on the qualitative research of capital account openness on financial stability and economic development. This paper tries to find the optimal degree of capital account openness based on current economic structure and financial situation through quantitative methods. I use Kalman filter to measure the degree of capital account openness based on the interest rate parity theory and defines the degree of capital account openness as λ, the range of λ is [0, 1]. For the measurement of financial stability, this paper adopts principal component analysis method, and selects 17 groups of data related to financial stability to measure it. Based on these two sets of figures, as well as monthly GDP data, establishes TVAR model, studies the impulse response graph, and determines the impact on financial stability index, GDP and λ. This paper uses support vector machine regression (SVR) to quantitatively analyze the relationship among capital account openness, economic development, and financial stability.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".