Законът на Оукън в България, Гърция и Русия: сравнителен анализ
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
Целта на настоящата статия е да се извършат емпирична оценка и сравнителен анализ на Закона на Оукън за България, Гърция и Русия. Чрез регресия на времеви редове по метода на най-малките квадрати е моделирана връзката между безработицата, икономическия растеж и производствения разрив в България и Гърция за периода от първото тримесечие на 2000 г. до третото тримесечие на 2019 г., а в Русия – за интервала от първото тримесечие на 2003 г. до третото тримесечие на 2019 г. Резултатите от емпиричния анализ показват, че докато в България фазата от бизнес цикъла не влияе на валидността и силата на проявление на Закона на Оукън, то в Гърция и в Русия връзката между безработицата и съвкупния продукт е циклично обусловена – тя е много по-силна по време на спад, отколкото в период на подем. Okun’s Law in Bulgaria, Greece and Russia: A Comparative Analysis The purpose of the article is to perform an empirical assessment and comparative analysis of Okun’s Law for Bulgaria, Greece and Russia. Ordinary least squares regressions of time series data (from the first quarter of 2000 to the third quarter of 2019 in Bulgaria and Greece, and from the first quarter of 2003 to the third quarter of 2019 in Russia) are employed to estimate the relationships between unemployment, economic growth and the output gap. The results from the empirical analysis show that while in Bulgaria the phase of the business cycle does not affect the validity and strength of the manifestation of Okun’s Law, in Greece and Russia the link between unemployment and output is cyclically influenced – it is much stronger during contraction than it is during expansion.
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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 | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.037 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.164 | 0.028 |
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