CONVERGENCE HYPOTHESIS: AN APPLICATION ON SELECTED OECD COUNTRIES
Bibliographic record
Abstract
Convergence in other words, with the closing of the gap between rich and poor economies, the large number of possible outcomes that may arise in the economy represents only one. While the countries in the middle income group are close to the rich, the poor countries are left behind. On the other hand, countries may experience a divergence in which rich countries are richer and poor countries are poorer than convergence as a whole. All of these possibilities are related to the change in per capita income distribution around the world. In this study, it was tried to be tested by panel unit root test methods that the growth of income levels of selected OECD countries (Argentina, Australia, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, Turkey, UK and USA) from 1961-2015 converged to each other. Findings from the study show that OECD countries converge on the convergence of national income to the US average national income in the mentioned period. Tests produced are consistent with each other in that the H0 panel unit root process can’t be accepted.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".