The Impact of Political Stability on the Effectiveness of the Early Warning Systems in Predicting the Financial Crises: The Case of Jordan and Qatar
Bibliographic record
Abstract
The research aims to identify the impact of political stability in determining the effectiveness of early warning systems in predicting financial crises. The research applied a standard descriptive approach.In general, when comparing the two countries before including the model for economic variables the results showed that the nature of the impact of economic variables is different as the index of the financial crisis in Jordan is affected by the import of goods and services while the most influential indicators in the early warning model for the occurrence of the financial crisis in Qatar is the index of exporting goods and services on the basis that the system Qatari financial is very sensitive to the subject of export of gas and oil. Also, the results showed that there is a very significant impact of political stability on the financial crisis, which is greater than the impact of economic indicators, and if the two countries differed in which indicators for political stability have the greatest impact on the occurrence of the financial crisis, in Jordan the most influential indicator was the government effectiveness variable in Qatar, the regulatory quality index was the most influential.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".