The Causality Relationships Between the Kingdom of Saudi Arabia’s (KSA) Military Expenditure and Economic Growth in the Period From 1987 to 2019
Bibliographic record
Abstract
This study aims to test the causality relationships between the Kingdom of Saudi Arabia’s (KSA) military expenditure and economic growth in KSA, by using time-series data in respect of the period from 1987 to 2019. This study applies the Granger causality approach and uses the ARDL bound test approach to test the long-term relationships between the variables. By applying the Granger causality approach, this study’s findings show that the rate of growth rate in KSA’s military expenditure does not result in a similar rate of growth in the country’s Gross Domestic Product (GDP). However, by applying the Granger causality approach, there is a bidirectional causality relationship between the KSA’s military expenditure and the fixed rate of the country’s capital growth.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".