Crude Oil and the Libyan Economy
Bibliographic record
Abstract
Oil is the primary source of income in the Libyan economy; hence, it is important to more fully understand the economic factors associated with this sector of the economy. We applied a recent growth theory model to develop a better understanding of the relationship among capital, labor, domestic consumption of oil, oil exports and gross domestic product (GDP) in the Libyan economy. A log linear model was estimated using annual data for the period 1980 to 2012. All of the coefficients were significant at the 0.05 level except the log of labor, which was significant at the 0.0613 level. The signs associated with the variables were consistent with economic theory. The adjusted R square was 0.912 indicating that approximately 91 percent of variation in GDP was explained by the independent variables. There was only limited multicollinearity in the model as all Variance Inflation Factors (VIF) values were less than 10. Breusch Pagan and Anderson-Darling test results indicated a constant variance and that the errors were normally distributed, respectively. Similarly, the Durbin-Watson statistic indicated an absence of autocorrelation at the 0.05 level. The resulting elasticities were positive and strongly inelastic, indicating that large changes in each of the variables would be required to dramatically increase GDP. Nevertheless, it is clear that oil will continue to play a leading future economic growth and development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".