Does the gross fixed capital formation represent a factor for supporting the economic growth
Bibliographic record
Abstract
The present study aims to show an analysis of relationship between gross fixed capital formation and economic growth in Romania, Bulgaria, Czech Republic, Poland and Hungary for the period 2003-2009. \nFor this, the statistical connection analysis method is applied. The used variables are: the economic growth (gross domestic product - GDP) – considered dependent variable and the gross fixed capital formation (GFCF) – considered independent variable. \nThis workpaper uses the quarterly of GDP and GFCF, considering the period from the last quarter of 2003 until the last quarter of 2009. That analysis indicates the relation between those two indicators separately for each country in order to draw a conclusion regarding the role of GFCF to the growth and development of the Central and Eastern Europe (CEE) countries and as well as its contribution to the formation of GDP. \nUsing this data, we apply the correlation analysis to verify the existence of the connection between two macroeconomic indicators. The obtained results show a direct and strong connection between economic growth and gross fixed capital formation, relation which is expressed by correlation coefficient with a level very close to the value of 1 for Romania, Bulgaria, Czech Republic and Poland. \nThe conclusion is that the level of the between gross fixed capital formation may influence in the positive way the economic growth, in Romania, Bulgaria, Czech Republic and Poland.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".