Financial Stability and Creating Financial Stability Index for Turkey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to create an index, which will be used for monitoring financial stability for Turkey. For this purpose, firstly the concept of financial stability was defined. As a result of the literature review, the variables considered to give information about financial stability were examined and the variables were grouped by principal component analysis. Finally, generated factors were subjected to logistic regression. The variables which were examined in the study include a total of 57 quarters, which is between the second quarter of 2003 and the second quarter of 2017. Three different dependent variables were used in the logistic regression analysis, each of which was formed by taking into consideration the “break points” in the independent variable series. All variables included in the financial stability index were standardized. The financial stability index was formed with the results obtained by the logistic regression analysis, and then; the contribution of the variables to financial stability was interpreted. It will be possible that the stakeholders in the financial system have information about the expected change in the level of financial stability by monitoring the variables which were examined and used in the analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it