Bibliographic record
Abstract
Economic growth is a prerequisite for economic development. However, there is no “recipe” for countries to create an environment of prosperity and to achieve high rates of economic growth. Many researchers have examined the drivers of economic growth and find that economic growth depends on many economic and institutional variables. In this context, the main objective of this paper is to examine the role of good governance on economic growth in piicgs countries (Portugal, Ireland, Italy, Cyprus, Greece, and Spain). The database was collected from many sources and the empirical analysis is based on a 2SLS (two-stage least squares) technique. In our empirical results, we find that trade openness, gross capital formation, inflation, political stability, rule of law, debt rule, budget balanced rule, and the combination between debt rule/budget balanced rule with political stability and combination between debt rule/budget balanced rule with rule of law are significant drivers of economic growth in piicgs countries while foreign direct investments, government effectiveness, voice and accountability, regulatory quality, fiscal rule index and expenditure rule are insignificant. However, the results may be different if we use other sample groups and/or different periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".